amirpouya sarraf

One of the important factors in the success of plans and projects is the role of machinery and equipment and the proper way to use and manage them properly. Knowing the types of drilling machines and equipment in dam construction projects and how to properly select and use them can play an effective role in the success of these projects; Due to the fact that the study in the field of machinery related to drilling operations and mainly construction machinery has a wide scope, in this research, traditional machines and machinery in this field have been evaluated and monitored.

 In this research, using the implementation method of PMBOK standard, sixth edition, and access to information on the construction site of the dam in question, as well as library studies, Internet search, existing documents, as well as field studies and visits to the dam site during construction to identify features. Drilling operations in dam construction projects have been performed with emphasis on the safety requirements of drilling machines and devices in the implementation stages, as well as the study of the project location and structural features of Haraz Dam.

The research findings indicate that the probability of occurrence in excavator (making noise in drilling and drilling activities) with a risk number of 56 and the severity of the effect due to the risks of falling rocks and materials and overturning during loading activity in the loader with Risk number 45 from the loading activity at the top of the category of unacceptable risks was determined that the risk of overturning the loader due to excessive fall during loading, excessive noise and vibration and vibration caused by it, as well as fire during operations In the excavator, the overturning of the dump truck during unloading due to the unevenness of the unloading site and the disturbance of its center of gravity is one of the most important risky activities, followed by the creation of unacceptable risks.

The results of this study indicate that the frequency of risks in dump trucks is higher than other devices, the probability of accidents in loaders and the severity of the effects of risks identified in excavators are higher than other devices in this field. According to the results of risk assessment, it can be concluded that most of the possible consequences are due to staff negligence and lack of continuous monitoring.

Global databases provide complete source of required precipitation information for users. The aim of this study is to investigate the accuracy of the TRMM, NCEP and MERRA global precipitation databases in estimating the daily and monthly precipitation in the Balkhaluchai catchment area of Ardabil province. Precipitation data of Nir station the three mentioned databases were extracted from 2002 to 2018. Mann-Kendall, Mann-Whitney and standard normal tests were used to check the trend and homogeneity of precipitation data. For three global precipitation databases, amount of R2, RMSE, NSE, RE and GMER were determined. The results of homogeneity test showed that all P-values were higher than 0.05 and the precipitation data were completely stochastic and homogeneous and no trends were observed in them. In both TRMM and NCEP databases, the R2 coefficient is good and is above 0.6. The MERRA database has had a weaker performance in this case. For the monthly period and the two TRMM and NCEP databases, the NSE index was higher than 0.5, which indicates the reliability of the precipitation data produced in these two stations. At the daily and monthly level, for all three global databases the GMER parameter values were obtained smaller than unity, which indicates that all global databases have overestimates. The overestimation of precipitation for the MERRA global database is much more severe than other two databases. MERRA database has a much weaker performance than NCEP and TRMM databases. In general, the TRMM database has a better efficiency than other databases.

Environmental vibrations can have a significant impact on noise pollution and the health of the residents of buildings adjacent the vibration sources. Therefore, the studies conducted in the last two decades on reducing vibrations have attracted a lot of attention from researchers. In this study, the effect of different types of improved trenches with Leca lightweight aggregate, including single and double-walled trenches in both active and passive modes, has been investigated, using the combination of Plexis3D finite element software and Python programming language, and the models with the results of other researches. It has been validated and also, a parametric study has been done to evaluate the effect of geometrical factors including depth, width and length for both systems. The obtained results indicate that in order to achieve an acceptable amount of trench efficiency, the depth of 0.8λr and 1λr is sufficient as the optimal depth for single and wall systems, respectively, and considering the effect of the width of the trench filled with Leca, a width equal to 0.2λr and 0.15λr are enough to reach the optimal width for single-wall and double-wall systems, respectively. Also, for passive design, the role of depth can be ignored for single and double-walled barriers, because trench width plays a more important role compared to trench depth. Moreover, to achieve an acceptable single-wall system, the normal width should be increased to about 0.2λr.

Sustainable use of fresh water resources and analysis of the hydrological behavior of the basin for water resources management requires knowledge of the state of water balance components. The main goal of the research is to estimate the water balance components of the Balkhlochai catchment area with an area of 567 square kilometers. Therefore, necessary information such as meteorological parameters, monthly discharge, as well as required maps were prepared and introduced to the model. In order to test and validate the model, SUFI2 algorithm was used. The period of 2003 to 2013 was considered for the validation period and 2014 to 2018 for validation. The results showed that the Nash-Sutcliffe and explanation coefficients were 0.52 and 0.60 respectively for the validation period and 0.60 and 0.65 respectively for the validation period. Also, the balance components of the basin showed that 69% of precipitation enters the atmosphere through evaporation and transpiration. About 16% enters waterways as surface runoff, lateral flow, and return flow, and 15% of it also infiltrates. The results of the research show the good performance of the SWAT model in simulating the water balance of the Balkhlochai watershed. One of the most important reasons for the decrease in the efficiency of the model can be attributed to the incompleteness of the snow melting coefficients, the less compatible initial assumptions of the model, for the simulation of water flow at high altitudes and frozen layers, and the lack of necessary information regarding the simulation of underground water cited.

Evaporation and Transpiration (including soil water evaporation and transpiration in plants) account for about 60% of the annual atmospheric precipitation on the earth. This process is the backbone of the hydrological cycle and a key element in Water Resources Management and the agricultural sector. The Soil and Water Assessment Tool (SWAT) and the Surface Energy Balance Algorithm for Land (SEBAL) are specialized models that address this problem. A SEBAL model is a tool for estimating the spatial distribution of actual evaporation and transpiration using satellite images such as MODIS, and Landsat 8. Although Landsat 8 images have a higher Spatioal resolution than MODIS images (30 m vs. 1000 m), their Tempoural resolution is lower than that of MODIS images (every 16 days vs. every day). On the other hand, daily MODIS images may not always be usable because of problems such as cloudiness. In addition, the interpretation of numerous satellite images is very time-consuming. In the current study, the evaporation and transpiration values in the catchment area of Karun River were obtained in three dry, normal and wet years (2019, 2020, 2021) using the recalibrated SWAT model based on runoff and crop yield and the SEBAL algorithm. Then the results of the SEBAL algorithm and SWAT model were compared with each other based on the water year conditions. In the second stage of the research, the results of the SEBAL algorithm and the range of changes of the main parameters of this algorithm in the plains of Ahvaz-Mlathani and Al-Baji basins were presented according to the ground data and using the MODIS images from the Terra satellite with a suitable time resolution and OLI sensor from Landsat8 satellite, which has a good spatial resolution.

Introduction and goal:

The phenomenon of flood is one of the frequent hazards that have caused financial losses and many lives in Iran in the last decade. One of the three main natural hazards of Iran is the occurrence of floods, and it is safe to say that a large flood occurs at least once a year in some part of this country. Floods are considered as a great threat to human life by damaging or killing people and animals, especially buildings and houses, agricultural land and crop production, urban infrastructure, bridges and roads. Floods in Iran cause a lot of damage from an economic point of view; it causes destruction of the environment, natural and residential resources and loss of life. In the past years, about 70% of the annual credits of the plan to reduce the effects of natural disasters and the headquarters of unexpected events have been used to compensate for the damages caused by floods. In this research, by using flood location data and using machine learning models and random forest data mining and the weight of evidence and their combination, the spatial prediction of flood prone areas has been discussed.

In this research, combined models and 11 predicting variables of flood probability in Karkheh watershed located in Lorestan province have been used. These variables include maps of geomorphometric indicators, including topographic humidity index, relative slope position and topographic position index, hydrological maps including: drainage density and distance from the drainage network, for this purpose, first, data mining models for the initial analysis of the relationships between environmental variables and events Past risks are used and their results are used as input data for machine learning models. Flood data were randomly divided into two groups: training (70%) and validation (30%). Prediction accuracy was evaluated using operating characteristic curve (ROC) method.

According to the results of the random forest model, the accuracy was 0.904, the weight of evidence was 0.886, and the combined model of random forest - the weight of evidence was 0.978. Based on the combined model of random forest and the weight of evidence as the superior model, 20.49% of the surface has medium upward potential. Based on the random forest model, drainage density, distance from waterways, height and land use were the most important factors affecting flood potential.

Considering the recent human activities and the resulting climate change in optimizing the operation of the dam reservoir, the effects of climate change should be noticed. In this research, in order to extract command curves by dolphin echolocation and gravitational search algorithms, the monthly inflow of the reservoir, the reservoir storage volume, and the downstream demand of the reservoir in case of climate change were calculated .The optimal output values of the reservoir of Lar Dam (located in Larijan, Amol City) were determined by the approach of minimizing the total square of the monthly relative deficiencies in supply demand and climate change conditions based on the river flow According to the research, by using HADCM3 and scenarios RCP2.6, RCP4.5, and RCP 8.5, climate change has increased the maximum temperature by 5%, 5.2%, and 6.2%, respectively. It has increased the minimum temperature by 3.5%, 5.6%, 5.17%, and increased precipitation by 8.5%, 9.5%, and 13%, respectively. In addition, the runoff from the intermediate scenarios indicates an increase of 3.3% compared to the base period. Moreover, to examine the water allocation policies required downstream, two future and basic conditions are considered. In this study, reservoir efficiency indices in the conditions of (future) climate change and their corresponding values ​​in the base period were compared. The execution results of each of the algorithms show that the execution speed of the DE algorithm is much higher than the GSA algorithm, as well as, in the conditions of climate change, the reliability index in the dolphin echolocation and gravity search algorithms has increased.  9.73 and 12.46% Vulnerability has decreased by 21.4% and 26.51%, respectively, and reversibility has increased by 18.27% and 17.64%, respectively. The execution results of each of the algorithms show that the execution speed of the DE algorithm is much higher than the GSA algorithm. Furthermore, in the conditions of climate change, the reliability index in the dolphin echolocation and gravity search algorithms has increased 9.73 and 12.46% Vulnerability has decreased by 21.4% and 26.51%, respectively, and reversibility has increased by 18.27% and 17.64%, respectively.

In recent years, use of Public-Private-Partnerships(3P) approach has received much attention to implementation of infrastructure projects. Risk identification and analysis in these projects has always been one of the main challenges of this approach. The purpose of this paper is to determine the different levels of risk in 3P water and wastewater projects in Iran using library studies and field surveys. Using a three-phase protocol and developing an integrated method of risk analysis, while identifying and separating risks, different levels of risk in these projects were evaluated. In the first phase, 49 risk factors were identified and divided into 6 main groups including technical(R1), economic(R2), political(R3), legal(R4), environmental(R5) and managerial(R6) risks. In the second phase, in order to extract more critical risks, the risks were analyzed and screened by combining Fuzzy Delphi(FD) techniques and Failure Mode Analysis and Effects(FMEA) using the opinions of experts. Based on the results, 23 sub-risk factors with RPNNormalized values greater than 0.5 were identified as critical risks. In the third phase, different levels of total risks(ORL) were determined by Fuzzy synthetic evaluation (FSE) method in combination with FMEA method. Based on the results of risk groups R2, R1, R3, R4, R6 and R5 with ORL of 7.468, 7.262, 7.132, 6.992, 6.815 and 6.670, respectively, as the most critical risk groups in 3P projects were identified. Risks of allocating financial resources and investor obligations (R2-6), lack of transparency in executive issues and changes in project specifications(R1-1), problems of the government system(R3-5), prolongation of licenses and their renewal(R4-6), integrated management, control and utilization of water resources(R6-8) and climate change and rainfall pattern(R5-2) in each of the above groups were identified as the most important critical risks. Finally, the ORL of 3P projects was set at 7.077, which indicates the high level of total risk of these projects.

The most important goal in the planning and optimal operation of the reservoir system is to determine the various operating policies that can operate properly Under the drought condition and existing uncertainties. In this study, a combination of the Horse herd optimization algorithm (HOA) and WEAP simulator model was used to extract the optimal reservoir exploitation policies in the form of specific optimization and objective functions were calculated based on the results of the implementation of each scenario and the total operating period for Maroon and Jarreh reservoir dams. The results showed that the average error of the optimal rules extracted from the support vector machines relative to the output of the HOA algorithm in the validation stage is less than 17%, which indicates the high efficiency of this method in predicting the optimal pattern of the dam control curve in real-time. Moreover, evaluation of different scenarios showed that agricultural development in areas 1, 4, and 5 of Ramhormoz will be reduced by an average of 50% and also a 10% reduction in inflow to Marun and Jarreh dams will have negative effects on Shadegan wetland.

As the biodiversity of valuable plant and animal species along the riversides –especially the ones located in the arid and semi-arid climate regions- has placed them in the category of sensitive and vulnerable ecosystems, awareness of these natural ecosystem's services could help protect them as much as possible, helping much more citizens benefit from their environmental services. Therefore, classifying different ecosystem services is necessary for identifying main natural ecosystem services, including regulatory, informative, productivity, and habitat services (De Groot et al., 2012). However, although many studies have already investigated issues such as improving the rivers' water quality, improving the riverbanks' landscape, and valuing the rivers' ecosystem services, no study has attempted to prioritize the rivers' ecosystem functions. Therefore, this study sought to do so in one of the Iranian largest rivers, which passes through several towns. In other words, the purpose of this study was to evaluate the function and significance of Atrak River's ecosystem functions as one of the most important northeastern rivers of Iran located in Maraveh Tappeh city, Golestan province, Iran.

As one of the most famous waterlogged rivers located in northeastern Iran with an arid and semi-arid climate, the Atrak River was selected to be investigated in this study. Originating from the high lands of Hezar Masjed mountains, the river is considered the fifth largest river in Iran, acting as the basis of economic activities in Golestan province, especially the Maraveh Tappeh city. Passing through different urban and rural areas, the river also connects different surrounding ecosystems that provide the residents of the neighboring cities with a variety of services (Sarraf et al., 2020), making the identification, weighing, and prioritization of the river's ecosystem functions via scientific models a highly important task.There are several methods to weight criteria via multi-criteria decision-making (MCDM) approaches, one of which is the entropy method introduced by Shannon. The method refers to a general measure of uncertainty, playing an important role in information theory. However, in cases such as interval data where the data are nondeterministic, the method must be modified to produce correct results. Therefore, this study used Shannon's entropy method to weigh the river’s ecosystem functions.Although the classical TOPSIS is widely used as a simple, reasonable, and flexibly applied calculation method, it has some shortcomings. For instance, the method could not rank the points in the perpendicular of the positive and negative ideal points. Thus, to overcome the shortcomings of the classical TOPSIS, the Improved TOPSIS has been developed as a new method to solve Multiple Criteria Decision-Making-related problems based on relative entropy. Accordingly, as the CODAS method acts more efficiently in resolving MCDM-related problems (Keshavarz-Gharabaee et al., 2016), the current study used the newest multi-criteria decision-making models (i.e., the Improved TOPSIS and the Combinative Distance-based Assessment (CODAS) approaches) to prioritize the river's functions.The required research data were collected via administering a questionnaire on 149 residents of Maraveh Tappeh in the summer and fall of 2020, asking them their opinion concerning the most important ecosystem functions of the river. Moreover, the Cochran formula was used to estimate the questionnaire's sample size. Also, an information brochure was enclosed with the questionnaire to familiarize the respondents with the Atrak River's ecosystem functions.

According to the results of weighting the river's ecosystem functions with the Shannon entropy method, the weights of habitat, productivity, information, and regulatory functions were found to be 0.2501, 0.2491, 0.2490, and 0.2478, respectively. Moreover, the results of prioritizing the Atrak River's ecosystem functions via the Improved TOPSIS and CODAS models showed that habitat, productivity, regulatory, and information functions ranked first to fourth in terms of priority, respectively. The study's results also showed that the habitat, productivity, regulatory, and information were considered as the most important functions in order of priority for 41%, 26%, 25%, and 8% of the residents, respectively.

Considering what was discussed above, it is suggested that the results of the present study be used as a model for designers and decision-makers of river engineering to implement appropriate plans to guarantee the sustainability of the river's ecosystem and the quality of the urban environment. As other studies such as the one carried out by Chaika et al. (2017) on the Florida River Basin have also found that habitat and productivity services are the most important rivers' ecosystem services from the riverbank residents' point of view, it could be argued that providing people, especially villagers and residents of the Atrak riversides, with the means of livelihood is considered as the highest priority. Therefore, preserving aquatic habitats and creating sustainable development in terms of fishing purposes could help fishermen and local stakeholders continue earning their livelihood.

By developing GIS and remote sensing technology, the widespread access possibility and local distribution of hydrological management parameters and variables have become practical. Runoff rainfall modeling is always an important and continuous need for practical issues in the fields of water resources evaluation, flood forecasting, engineering canals designing, and many other goals (Bone, 2001). Calculation of runoff-rainfall results has been made practical and operationalized by using GIS techniques and a distributed hydrological model. The WetSpa runoff-rainfall model is a hydrological-distribution model that was developed in Brussels in 1997 (Wange et al, 1997) and also, had been used in various research and executive projects by developing in various models. This hydrological model has the capability of performing simulations at the pixel level and because of this, it provides the possibility of using aerial and satellite images with accurate information measured at the basin level, which has a local distribution (Hooshyarypor at el, 1397). Due to the high uncertainties of hydrological parameters, the calibration model is one of the most important part of modeling whose optimization techniques are mainly are used for such purposes. There are various methods for optimization, sensitivity analysis, and also evaluation of uncertainty of models. Accordingly, this paper’s purpose is to calibrate the WetSpa hydrological model with a multi-objective optimization approach that uses the Social Spider Algorithm (SSA) and the Black Widow Spider (BWO) techniques. By this point of view, to achieve a reliable prediction, in addition to the usual discharges, the model must be able to predict high and low discharges accurately (including maximum and minimum), so the objective functions are selected in a way that the best match between observational and computational values can be achieved during the Vasanji process.

WetSpa Model: WetSpa is a continuous local and temporal model in which all of the simulations are conducted continuously. The WetSpa model displays the water and energy balance for each calculation cell, considers rainfall processes, vegetation, snowmelt, wetting, infiltration, evapotranspiration, leakage, surface runoff, wall flow, and groundwater flow. The hydrological system simulated by this model consists of four layers: vegetation, soil surface, root zone, and saturated groundwater table.The Optimization algorithms: The Social Spider Optimization (SSO) algorithm is a new optimization approach which is proposed in 2013 by Kause et al. Another optimization approach used in this paper is the Black Widow Spider (BWO) optimization algorithm. During the day, the black widow spider is out of sight and is mostly nocturnal, and rotates its network during the night. Generally, the widow spends most of her adult life on the same site (Andrite and Banta, 2002).Objective functions and model evaluation: In this paper, to evaluate the model, five statistical indices have been used of correlation coefficient (r), Root Mean Square Error (RMSE), mean absolute error (MAE), Nash-Sutcliffe index, and Nash-Sutcliffe logarithmic index. To increase the accuracy of the model in predicting the minimum and maximum flows, two alogarithm Nash-Sutcliffe and Nash-Sutcliffe logarithmic criteria were used.Zashk basin and model data: Zashk Basin is located in Khorasan Razavi province and in the west of Mashhad with an area of 65.56 square kilometers.

In this part of the paper, the calibration results of the WetSpa model using social spider and black widow spider algorithms are presented. The problem decision variables are the 11 global parameters illustrated in Table 1. Objective functions must be selected in a way that at the end of the calibration process the best match between the observed and computational values can be obtained. Some of these functions give more weight in high flows, while others consider more weight in low flows and have more emphasis on them. In this regard, Nash-Sutcliffe (NS) criterion and its logarithmic form (NS-Log) have been used. In this paper, the results obtained based on these two functions are illustrated. After determining all the necessary local networks in basin modeling, precipitation, evaporation, temperature, and discharge information from 2009 to 2011 were used to calibrate the model and 2012 to 2014 data were used to validate the results. Each of the optimization algorithms was conducted with an initial population size of 100 people and over 100 generations (number of times the optimization model was executed). As it was observed, the answers obtained by BWO are better than the answers of SSO due to its higher NS values. According to the obtained results, NS and NS-Log values are generally from -2.3 to 0.75 and from -0.165 to -0.01, respectively. This problem illustrates that the calibrated model has been more successful in simulating low discharges. In fact, since the model has been executed continuously, the simulation results in 2008 are considered as the Warm-Up period.

This paper calibrated the WetSpa distributed rainfall-runoff model. To calibrate the model, two evolutionary optimization algorithms of social spider (SSO) and black widow spider (BWO) were used in the Zashk basin of Mashhad. The results obtained from the usage of the model in this basin illustrated the satisfactory capability of these algorithms in calibrating the WetSpa model. Comparing the results obtained from the SSO and BWO algorithms illustrated that in the multi-objective calibration problem, the BWO algorithm was slightly more successful than the SSO model. Also, the results indicated that the simulation quality of low discharges was higher than the high ones. The reason for this problem, firstly, can be due to the lower abundance of high discharges in the evaluated data set and secondly, the relatively slow response of the model to changes in hydrological conditions in flood conditions in the catchment basin, because the temporary groundwater storage coefficient (K5) allocated a large amount for itself that can directly affect the reduction of surface runoff. On the other hand, the weakness of the results in predicting some low discharges can be related to the optimal value of groundwater recession coefficient (K2), which has taken a small amount in the calibration process. The results of this study illustrated that the effect of the surface runoff coefficient on the model results is much greater than other parameters (about 36%).

In the present study, a multi-objective Dolphin Echolocation Algorithm (MODEA) was used to derive optimized operation rules of Gavoshan dam in kermanshah province under climate change conditions. These rules were extracted for the two purposes of minimizing vulnerability and maximizing reliability indices under the baseline i.e., April 2007 to October 2019 and climate change conditions i.e., April 2040 to October 2052. Results showed that under climate change conditions, in addition to increasing temperature by 1.8 degrees Celsius and decreasing in precipitation by 20.1%, the runoff would also be reduced to 0.69 related to the baseline period. Moreover, vulnerability changes by 18% to 45% and 10% to 39% in baseline and climate change conditions respectively, and the range of reliability changes by 52% to 89.5% and 28% to 90%, under these conditions, respectively. Comparison of the release and the water demand volume under climate change to the baseline conditions -based on the Pareto point with 80% reliability while increasing the release rate- indicates a greater adaptation of the release rate from the reservoir to the demand volume and a better performance of the reservoir under climate change conditions.

River runoff forecasting in watersheds has a special place in the management and planning of water resources for the design of water facilities, water intake from rivers, consumption management and etc. In the present study, the performance of some data integration models including simple averaging, weighted averaging and integrated artificial neural network model in monthly discharge modeling has been evaluated and compared. For this purpose, monthly flow prediction in upstream basin of Jiroft Dam was examined using Artificial Neural Network (ANN) models, Adaptive Neural-Fuzzy Inference System (ANFIS), ARIMA model and Support Vector Regression (SVR) model as an individual model. Then, the individual models were trained and validated using selected predictor variables and their results were selected for use in the integration process. Large-scale climatic signals including NAO, ENSO and PDO are also used in hydrological forecasts of river flow and the performance of single and integrated models in two modes with and without considering these signals has been compared based on the evaluation of three criteria Nash-Sutcliffe (NSE), Coefficient of determination (R2) and Mean Square Error (MSE). Results of this study indicated that the integrated approach significantly increases the accuracy of predictions. In addition, large-scale climatic signals were found to improve results, especially during the test period. For example, the results of the integrated model of artificial neural network with large climatic scale signals show that this model has the best performance among the integrated models. Also, the NSE criterion has improved by 0.04 in training compared to the integrated model of artificial neural network without large-scale signals and the MSE error has been reduced by 0.001.

Conceptual rainfall-runoff (RR) models are one of the simple and efficient tools in hydrological modeling. These models simulate the flow regime using mathematical equations using input data such as precipitation, evapotranspiration and measured temperature, and basin topographic information. Calibration of RR models, e.g. WetSpa which has been developed in Belgium, is a process in which parameter adjustment are made so as to match the dynamic behavior of the RR model to the observed behavior of the catchment. This research presents an application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Black Widow Optimization (BWO) for multi-objective calibration of WetSpa in Gorganroud river basin, Iran to optimize 11 global parameters of the WetSpa model. The objective functions are Nash–Sutcliffe and logarithmic Nash–Sutcliffe efficiencies in order to improve the model's performance. The WetSpa model then was applied for a period of 1-year flood simulation in the basin and the results were analyzed. Results showed that the evolutionary NSGA-II and BWO algorithms are capable of locating optimal parameter sets in the search space. The measured correlation coefficient in the calibration process was 0.69 and 0.81 for the NSGA-II and BWO algorithms, respectively. Moreover, a sensitivity analysis was conducted on the global parameters in which the surface runoff coefficient was the most sensitive parameter of the model.

TThe amount of runoff entering the dam’s reservoirs are continuously affected by climatic parameters, which influenced by the climate change phenomenon. In this study, the climate change parameters were obtained based on CANESM2 climate model using the SDSM4.2 statistical downscaling model. Then the rainfall-runoff process was simulated by ANFIS model with Sugeno structure and subtractive clustering at the entrance of Golestan dam reservoir in climate change conditions. Finally, the Improved Multi-Objective Whale Optimization Algorithm (MOIWOA) which is a combination of Whale V (WOA) and the Differential Evolution (DE) is used to extract the optimal operation rules of Golestan Dam Reservoir in Golestan province. The results of the uncertainty analysis indicated that the simulation results of the climate change period were in the 95% confidence band - in both calibration and validation phases. Also optimization of Golestan reservoir in baseline (March 2005-September 2018) and climate change (April 2021-October 2033) periods showed that the vulnerability changes in the baseline and climate changes are in the range of 18-45% and 10-39%, respectively, and the reliability ranges over 52 to 89.5% and 28 to 90%, respectively, in both baseline and climate change phases. And for 80% reliability, the baseline and climate change conditionschr('39') vulnerability are obtained as 31% and 27%, respectively. Comparison of the optimal rules derived from the baseline conditions with the optimized ones from the climate change showed that the plan water demand is met by 80% reliability index. In addition, the release volume in climate change conditions is higher than its baseline one, which can be due to the increased volume of water demand in climate change conditions. On the other hand, comparing the performance of the reservoir to meet the irrigation demands of downstream land at the Pareto point (80% reliability) in terms of baseline and climate change also suggests a greater adaptation of reservoir release to demand in climate change period.

It is inevitable to use optimization methods for multi-objective reservoirs' operation. In this research, after introducing the whale multi-objective optimization algorithm, its performance as optimal operation of the Boostan dam reservoir was evaluated based on the Conflict Resolution Method of Kalai-Smorodinsky. In the present optimization problem, while applying the constraints of reservoir continuity equations, the objective functions were defined as minimization of downstream water demand deficiencies and minimizing reservoir volume for flood control during the operation period. The performance of the proposed algorithm was compared with NSGA-II as one of the common algorithms in this field. Model performance was compared and evaluated based on reliability, reversibility, vulnerability and sustainability indices. To solve the conflict between the goals, the game theory-based method (Kalai-Smorodinsky method) was used to find the optimal solution. The optimization results showed better performance of the whale multi-objective algorithm than NSGA-II, in both objective functions. In terms of optimization time, the multi-objective whale algorithm converges faster than NSGA-II because of the fewer regulatory parameters. The model performance evaluation indices show that whale algorithm reliability is higher (86%) than NSGA-II. Moreover, the results of Shannon entropy method for weighting the indices showed that the model evaluation parameters are more weighted than the objective functions. The results of the Kalai-Smorodinsky method showed that the operation policies of Boostan Dam Reservoir have got a very good agreement with the whale multi-objective algorithm. On the other hand, since the most and the least desirable objective function values estimated by the whale multi-objective optimization algorithm are less than the other one, the performance of the whale multi-objective algorithm can be more appropriately identified.

Climate evolutionary theory reveals that climate change has already been evident in the planet's history, but when opposed to historical climate changes, the climatic changes of the last century have two unique characteristics. First, through the nature of the ongoing climate change, human actions play a significant role Second; the speed of recent climatic changes is greater, so that, many changes will be occurring in the Earth's atmosphere during a short term [Telmer et al. 2004]. Nowadays, global warming has significant effects on precipitation and runoff yield and water resources due to the increased concentration of greenhouse gases [Donyaii et al. 2020]. The average meteorological parameters, in particular the annual or seasonal components of temperature, precipitation and runoff, play a significant role in the hydrological cycle and are typically used as an indicator for climate change evaluation on the water supplies available to Iran now and particularly in the future [Donyaii et al. 2020]. Based on the IPCC Fourth Assessment Report Models [AR4], a number of studies have been undertaken to examine the effect of climate change on the hydrological components of watersheds in Iran. In contrast with the Fifth Assessment Study [AR5] models, these models, along with older pollution scenarios, have limited resolution. Therefore, in the watersheds of Iran, climate change experiments with higher resolution climate models under the latest pollution scenarios [RCPs] of the AR5 seem appropriate. According to historical evidence of Gorganroud's high flood capacity in the province of Golestan, Iran, the recognition of the impact of climate change on the watershed's hydrological regime is important for water resource planners.

2-1- Study area and data set The Gorganroud Watershed is located in Golestan Province, Iran. In this study, the Soil and Water Assessment Tool [SWAT] was employed for hydrological simulation of the watershed based on the downscaled outputs [using the Bias Correction and Spatial Disaggregation [BCSD] method] of fifth assessment report climate change model [MIROC-ESM] for historical and future periods. The trend analysis of hydro-climatic records was done according to the non-parametric Mann-Kendall test. The future projection was conducted for the near [2025-2050], mid [2051-2075], and far [2075-2100] future periods related to historical records in the period of 1985-2005. 2-2- SWAT set-up and calibration, validation and uncertainty analysisIn this study, runoff was estimated using the Soil Conservation Service [SCS] method. The Manning equation and Muskingum method were utilized to calculate flow velocity and routing phase, respectively. On the other hand, the SUFI-2 algorithm was employed to calibrate and analyze the sensitivity, and uncertainty of the SWAT model. The sensitivity analysis is based on linear approximation and the degree of uncertainty is calculated by two factors called r-factor and P-factor. The calibration and validation were performed using runoff data in the periods of 1995-2015 and 2016-2019, respectively. The coefficients of determination [R2] and Nash-Sutcliffe [NS] were used as the objective function to determine the goodness of fitness.2-3- Climate Change scenarios and AR modelsدIn the AR5 new emission scenarios based on emission forcing level until 2100 were employed. In order to investigate the future climate change, the Model for Interdisciplinary Research on Climate-Earth System Models [MIROC-ESM] was selected among the newest extracted models presented in the AR5, because the result of this model in Gorganroud watershed showed the highest agreement with observational data. This model consists of four emission forcing scenarios [RCP2.6, RCP.4.5, RCP6.0 and RCP8.5.

3-1- SWAT sensitivity analysis, calibration and validation analysis Seventeen parameters were chosen for SWAT sensitivity analysis using the 500 simulations of SUFI-2. Results showed that the parameters CN, SOL_BD and SOL_K have the highest relative sensitivity. Based on the results, the coefficients R2 and NS for runoff simulation were estimated to be 0.79-0.77 and 0.74-0.71 in the calibration and validation stage, respectively. Therefore, the results of the model are acceptable and its uncertainty metrics is satisfactory in general. The study results showed that the model has estimated the amount of peak discharge less than the actual amounts, which is confirmed by the average monthly simulated discharge during calibration and validation periods. The results also showed that more than 50% of the observational data in both calibration and validation phases are bracketed by the 95PPU uncertainty estimation band, which indicate a rather acceptable degree of certainty in simulation. 3-2- Climate change simulation results and trend analysis In the near and mid-future, there are increasing changes under the RCP2.6 scenario, but the trends of rainfall are not statistically significant at the 5% level. In the far- future a significant increasing trend isobserved under the RCP2.6 scenario, meanwhile in far-future under the RCP4.5 scenario there are increasing changes, but the trends are not statistically significant. In the mid and far future under the RCP6.0 scenario, a significant increasing trend has been observed. Finally, in the mid- future under the RCP8.5 scenario, there is a significant increasing trend. However, the increasing changes in the near and far-future periods are not statistically significant at the confidence level of 95%. The trend analysis of variables indicates that the amount of rainfall will decrease in this watershed during the future periods by the end of the 21st century. The most decreasing alterations in the rainfall and the highest increase in the temperature are achieved under the highest concentration of greenhouse gases [RCP8.5]. Moreover, in the near, mid, and far future, the runoff changes are decreasing under the RCP2.6 scenario, but the trend is not statistically significant. In the mid and far-future periods under the RCP4.5 scenario, there is a statistical significant decreasing trend in runoff; however, the decreasing variation in the near future is not significant. In the near, mid, and far future under the RCP6.0, runoff variations are declining, but the trend is not statistically significant. In the far-future period, under the RCP8.5, there is a significant decreasing trend; however, in the near and mid-future, runoff declining changes are not statistically significant. Reduced rainfall and increased temperature in the watershed will reduce the rate of runoff in the future periods in such a way that the security of the inhabitants of the region will be severely affected.

Results of evaluation criteria [R2 and NS] showed that the SWAT performance for the simulation of runoff in the Gorganroud watershed was not satisfactory, but it was in an acceptable range. Climate change simulation indicated a decreasing trend for rainfall in all future periods, but this trend was not statistically significant. The temperature variable in all RCPs had an increasing trend. However, temperature trend analysis under the RCP4.5 scenario during the near and mid- future and under the RCP6.0 scenario during the near, mid, and far-future showed a significant upward trend. Runoff under the RCP4.5 scenario during the mid to far-future and under the RCP8.5 scenario during the far-future period followed a significant downward trend. Runoff during the near-future period under the RCP4.5 scenario and throughout the near to mid-future under the RCP8.5 scenario, had declining variations, but its trend was not statistically significant. In general, these results indicated that the amount of temperature will follow an increasing tendency; while rainfall and runoff will follow a decreasing movement in this watershed by the end of the 21st century.

Iran's location on the arid and semi-arid belt of the world, as well as the mismanagement of water resources, has created a warning situation of water shortage in many parts of the country. The present research evaluates the effects of climate change on temperature, rainfall and runoff in future periods with the help of LARS-WG statistical model and SWAT hydrological conceptual model for Lar Basin. To estimate the flow rate of the river, the performance of Bayesian network and the combined wavelet-neural network model are also examined. After entering the rainfall and temperature information of the region, runoff was simulated for two hydrometric stations of Gozeldareh and Plour and the outflow runoff of Plour station between 1979 to 2018 was calibrated and validated as a control point. In order to evaluate the efficiency of the models, the criteria of Nash-Sutcliffe and explanation coefficient are used. According to climate models, the highest temperature increase in the final period and under the RCP8.5 climate scenario shows about 10% increase in temperature in spring and winter. Finally, among these models, the physical model with an average annual prediction of 6.04 cubic meters per second according to the observation period, showed a decrease in runoff

Changes in meteorological and hydrological patterns have led to the use of water resources management tools to find a suitable solution for optimal reservoir operation. Regarding to the inability of conventional optimization methods in solving the complex optimization problems, the use of meta-heuristic algorithms has been considered more than before.

In the present study, a combined model of Crow Search (CSA) and Gray Wolf (GWO) Optimization algorithms called Gray Wolf - Crow Search Hybrid algorithm was introduced for the first time in the field of reservoir operation optimization. And its performance was evaluated in comparison with its constituent elements as a powerful tool for optimizing the operation of the single reservoir system of Golestan Dam, considering the objective function (providing downstream water demand). To compare the convergence and performance of these algorithms, the statistical parameters of each algorithm were calculated and compared with each other, as well as with the solution of non-linear problem solving model (i.e., GAMS Software). Then, in order to analyze the performance of the algorithms, the Combinative Distance-based Assessment (CODAS) Multi-Criteria Decision Making Model was used to rank the decision alternatives (e.g., optimization algorithms) based on volumetric and time based reliability, reversibility, vulnerability criteria and the optimized objective function.

The results suggest that the GWOCSA hybrid approach has a response closer to the absolute optimal value, with an average response rate of 93% of the absolute optimal response and an average of 92% and 83% of the GWO and CSA ones. In addition, the correlation coefficient in the hybrid algorithm is 23 and 1.67 times lower than that of the gray wolf and the crow search algorithm, respectively. On the other hand, the GWOCSA hybrid algorithm performs the best, except in terms of reversibility index in other indicators. The CODAS Multi-Criteria Decision Making Model also identified the GWOCSA algorithm as the first to solve the problem of the reservoir operation compared to the other two passive algorithms. The gray wolf and the crow search algorithm then rank second and third, respectively.

The CODAS Multi-Criteria Decision Making Model identifies the GWOCSA algorithm in optimizing the objective function better than its constituent algorithms, namely CSA Optimization and GWO algorithm. And the gray wolf and crow search algorithm are then ranked second and third, respectively, so that not only is GWOCSA better at finding the optimal answer, but it also improves performance and increases the efficiency of the hybrid algorithm according to the model performance evaluation indicators.

In this research, after introducing the Whale, Cuckoo search and fruit fly Multi-Objective Optimization Algorithms, their performance individually and compared to each other was evaluated to optimize the Golestan Dam reservoir operation policies with an approach to reduce downstream water demand shortages and flood storage volume management based on reliability, resilience, vulnerability and sustainability criteria. The results showed that the proposed algorithms act differently in optimizing the objective functions. So that the whale multi-objective optimization algorithm leads to better values of objective functions. Regarding to the optimization time, although, there are no significant differences between the cuckoo search and whale algorithms, both perform far better than the fruit fly algorithm and achieve much more convergence over time. When comparing the whale algorithm, as the most efficient algorithm, with the fruit fly algorithm, as the most inefficient algorithm, the model reliability and resilience indices increased by 44% and 52%, respectively, and its vulnerability decreased by 23%, indicating a better performance compared to the other algorithms. Therefore, Whale multi-objective optimization algorithm performs better and converges better than the other algorithms, and cuckoo search and fruit fly multi-objective algorithms ranked second and third.

The importance of the proper implementation of the Groundwater Revival Plan (GRP) as one of the key approvals of the Supreme Water Council has been doubled due to the escalation of the groundwater crisis in most of Iran's aquifers. Therefore, the implementation of GRP principles is the only way to overcome the crisis and the related challenges. In this study, the management crisis of Iran's groundwater resources is re-evaluated from the perspective of the administrative, social, and legal system with the GRP challenging approach. Then, the best -worst fuzzy multi - criteria decision model in Lingo9 software is developed by collecting the opinions of 23 GRP experts, based on three general criteria of change in administrative structure (C1), policy structure (C2) and modification of GRP legal structure (C3), which includes 18 sub-criteria. The results showed that timely and appropriate allocation of financial resources (S22), creating incentive packages for farmers to improve the cultivation pattern (S23) and creating serious efforts at all levels of management in the field of implementation, continuity, and removal of obstacles (S21) respectively with weights. 0.2247, 0.1098, and 0.0946 - with more than 40% of the total importance - occupy the first to third ranks.

The application of optimization tools and techniques to operate the reservoir on a Multi-objective basis under the circumstances of climate change is unavoidable. The present study utilizes the Multi-Objective Farmland Fertility Optimization (MOFFA) algorithm to derive optimum rules on the operation of the Golestan Dam in Golestan province under circumstances of climate change. The two targets of reducing vulnerability as well as maximizing reliability under baseline conditions (from April 2006 to October 2018) and climate change conditions (from April 2021 to October 2033) have been formulated for such guidelines. Results revealed that under climate change circumstances, the river flow decreased by 0.17 percent of the baseline period, although the temperature was increased by 20% as well as the rainfall decreased by 21.1%. However, the extent of vulnerability variations in baseline and climate change was 16-45% and 10-43%, respectively. The range of reliability variations in baseline and climate change circumstances was 47-90% and 27-93%. The vulnerability has also been measured at 29 percent and 27 percent for baseline and climate change, respectively, with 75 percent reliability. The increase in release rates for climate change in comparison with baseline circumstances and higher modification of release rates from the reservoir to demand and stronger dam efficiency in changing circumstances showed the comparison of releases and the water shortage requirements for each of Pareto points.

In this study, the performance of the algorithms of whale, Differential evolutionary, crow search, and Gray Wolf optimization were evaluated to operate the Golestan Dam reservoir with the objective function of meeting downstream water needs. Also, after defining the objective function and its constraints, the convergence degree of the algorithms was compared with each other and with the absolute optimal values obtained from GAMS nonlinear programming method (19.41). These values together with each algorithm optimization results were ranked using six multi-criteria decision-making methods of TOPSIS, VICOR, Linmap, Codas, ELECTRE and Simple Additive Weighting after obtaining the performance evaluation criteria of each algorithm (Reliability, reversibility, and vulnerability). Finally, integration methods (Mean, Borda, and Copland techniques) were used to evaluate the performance of decision models. The results showed that the mean responses of the gray wolf, the whale, differential evolutionary, and crow search algorithms were 1.08, 1.49, 1.29 and 1.19 times the absolute optimal response and the answers’ coefficient of variation obtained by Gray Wolf algorithm was 113.2, and 1.43 times smaller than the whale, differential evolutionary, and crow search algorithms, respectively. Moreover, all integration techniques indicated the superiority of the gray wolf algorithm. Then, the Crow search, Differential evolutionary, and whale algorithms were ranked second to fourth, respectively. On the other hand, the use of these methods in solving the problem of Golestan Dam reservoir optimization was considered appropriate due to the similarity of the results obtained from the integration techniques with the results of TOPSIS, VICOR and Linmap methods.

Today, due to the importance of sustainable groundwater management, groundwater level modeling and forecasting are used to assess and evaluate water resources. The purpose of this study is to evaluate the performance of two models of Extreme Learning Machines (ELM) and Artificial Neural Network (ANN) and the combination of two models with wavelet transmission algorithms (W-ELM and W-ANN), which ultimately to increases the predictive power and optimization of input weights (the weights between the input and hidden layers) of models, Quantum Particle Swarm Optimization algorithm (QPSO) has been used. Also, in this study, the data of Ground Water Level of observation wells (GWL), precipitation (P) and average temperature (T) of Urmia Plain aquifer with a time series of 36 years (1981 – 2017) which were collected on monthly scale, are used. Also, in order to evaluate the performance of models, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used. In this regard, 80% of the data (September 1981 to August 2010) are used for training section and 20% of data (September 2010 to August 2017) used for the test section of models. Based on the results of this study, the hybrid model of W-ELM-QPSO with correlation coefficient (R) 0.991, 0.983 and 0.975, respectively for periods of one, two and three months in the test section, have a better performance than other models and also in addition to predicting power, this model has a high speed in terms of training and testing speed than other models.