مجله مهندسی و مدیریت آبخیز
سال پانزدهم شماره 3 (پاییز 1402)

The cycle of soil erosion (including removal, transport and deposition) that controls the sedimentation of watersheds, includes a set of complex and highly nonlinear processes. On the other hand, the factors influencing sedimentation in watersheds are very diverse, and according to the specific conditions of climate, soil, vegetation, geology, topography, etc., in each basin, the weight and role of each of the mentioned factors in sediment production is very different. Accurately determining and measuring these factors and making mathematical relationships between them are often difficult, expensive, time-consuming and error-prone, and this is the case with the use of models based on computational intelligence and the use of a limited number of basin dynamic variables, it is possible to simulate the behavior of the watershed in sediment production. Regardless of the type of intelligent models, in most of the conducted research (especially in internal research), the simulation of suspended sediment is mainly based on the discharge variable and the role of variables such as precipitation (especially precipitation obtained from satellite images), which are effective in the sedimentation of basins, have received less attention. In addition to precipitation, the skewness of sediment measurement data is also one of the issues that lack of recognition and attention will reduce the efficiency of estimator models. In the present study, the role of variable daily rainfall (taken from CHIRPS satellite) in the simulation of suspended sediment of Qarachai River has been investigated.

Multi-layer perceptron artificial neural network was used in order to simulate the daily suspended sediment concentration of Qarachai River (at the Ramian hydrometer station in Golestan province). In this regard, the variables of discharge and previous discarge (in instantaneous and daily scales) as well as the average daily and previous rainfall of the basin (taken from CHIRPS satellite) for a statistical period of 37 years (1980-2017) as variables model input was used. In order to increase the generalization power of the models, self-organized mapping neural network (for data clustering) and gamma test was used to find the best combination of input variables. In order to improve the efficiency of network training, a variety of activation and loss functions as well as the overfitting prevention algorithm were used. In order to investigate the effect of using activation and loss functions in suspended sediment estimation, different scenarios were considered, which led to the construction of 9 models. After that, using validation indicators, the effectiveness of the models in simulating suspended sediment was investigated and compared, and then the best model was selected.

The results obtained from the present research showed that among the different models, the neural network model with Huber's activation function and ReLU loss function, having the average absolute value of the error equal to 368 mg/l, the root mean square error equal to 597 mg per liter, the Nash-Sutcliffe coefficient of 0.87 and the percent bias -2.2% were selected as the best model. The results also showed that the use of the rainfall variable (as one of the important factors in causing erosion and sediment transfer in the basin) has improved the efficiency of the models, therefore, considering the ease of using CHIRPS satellite rainfall data, it is suggested in order to simulate the suspended sediment of rivers, this data is also used along with other predictive variables.

In the simulation of suspended sediment, discharge variable is often used as the only predicting variable of suspended sediment, while in basins with rainy, or rainy-snow regimes, the role of precipitation in the production of surface runoff and soil erosion is very important and plays an important role in the production and transport of sediment in the basin. In this regard, although the use of rainfall data obtained from ground rain gauge stations has played an effective role in increasing the efficiency of data-based models in estimating suspended sediment, however, the preparation of hundreds of spatial distribution layers of daily rainfall from the data point data of ground stations, the use of this variable in the simulation of the suspended sediment of the basin has been faced with many problems (such as the lack or inappropriateness of the spatial distribution of rain gauge stations, statistical deficiencies, the use of inappropriate interpolation methods and time-consuming calculations). Therefore, in practice, the variable of river flow is often used as a predictor of sediment, and precipitation is used less often. One of the solutions to the problem mentioned in the present study is the use of CHIRPS satellite data, which was investigated for the first time in this study. These data, available since 1981, can easily be used to simulate suspended sediment or other applications related to watersheds. Another important point that needs to be taken into account in the simulation of suspended sediment is the presence of high skewness in sediment measurement data (both suspended sediment and flow rate), which lack of attention in the process of training (or recalibration) and testing the models leads to It will lead to the construction of weak models in terms of efficiency and the existence of uncertainty in the accuracy of their results. In this regard, it is necessary to use logarithmic transformations or suitable functions of activation and loss in the training process, which in this research, two functions, ReLU and Huber, were proposed respectively. Another important point is to pay attention to the generalization power of data-based models, which is largely dependent on the data used in their calibration or training process. These data should be selected in such a way that while they are representative of the data in the entire statistical period, they are similar and have the same distribution with other data sets (such as cross-validation or test sets). According to the results obtained from the present research and in order to increase the efficiency of artificial neural network models in estimating the suspended sediment of watershed hydrometric stations, it is suggested to use the experiences obtained in this research in other sediment measuring stations of the country.

The availability of water for agriculture is of great importance, and despite the water crisis that is becoming more severe every year, both quantitatively and qualitatively, this issue should be seriously considered. Water resources include surface and groundwater, which are qualitatively more at risk, therefore, in order to preserve them, the sources of pollution must be known and appropriate solutions must be provided to prevent or eliminate these pollutions.

In this research, phosphate transfer cycle in Zanjanrood Watershed has been simulated using SWAT model. For calibration and validation, SWAT-CUP software and measured values ​​of average monthly current intensity at Sarcham hydrometric station between (1996-2013) were used and 26 sensitive parameters were selected for sensitivity analysis. There are three options for irrigation method, three options for fertilizer application and two combined options. In order to analyze the uncertainty of the indicators p-factor and r-factor and to analyze the quality of the model results, two indices of coefficient of determination (R2) and nash-sutcliffe coefficient (NS) have been used.

In the monthly runoff calibration stage, at the output of the field, the coefficients of r-factor, p-factor, R2, NS were 0.27, 0.11, 0.83 and 0.53, respectively, and in the validation stage were 0.60, 0.18, 0.73 and 0.53, respectively. The results showed that with increasing the level of pressurized irrigation, the amount of phosphate contamination at the outlet of the basin did not change significantly. Regarding the amount of fertilizer, the 50% reduction in the consumption of phosphate fertilizers has reduced the amount of phosphate entering the Zanjanrud River by 19.2%. On the other hand, a 50% increase in the use of fertilizers has increased the input phosphate by 17.7%.

The results showed the proper performance of the SWAT model and its ability in the mentioned simulation. Also, by changing the surface irrigation method to subsurface and increasing the irrigation efficiency, there is no significant change in the average amount of phosphate output from the basin. On the other hand, by reducing the amount of fertilizer and preventing improper fertilization by farmers, pollution of surface and groundwater resources can be greatly prevented.

Floods are one of the most destructive natural disasters that cause severe injuries and loss of life, major infrastructure damage, significant economic losses, and social unrest worldwide. Due to the fact that flood is a dynamic and multidimensional phenomenon, Geographic Information System (GIS) and Remote Sensing (RS) data are used to a large extent to discover the extent of flooded areas and play a special role in preparing flood risk and susceptibility maps. Flood susceptibility mapping is essential for characterizing flood risk areas and planning flood control schemes.

In this research, the identification of flooded areas in the Karun Watershed based on the Analytical Hierarchy Process (AHP) in the GIS environment and its validation with the NDWI blue index extracted from Landsat 8 satellite images has been considered. For this purpose, first, 15 effective parameters in floods occurrence including slope, aspect, elevation, curvature, rainfall, distance from stream, stream density, distance from fault, fault density, distance from road, road density, lithology, Curve Number (CN), land use, Topographic Wetness Index (TWI) and Stream Power Index (SPI) were selected and the weighting of these parameters was done based on AHP method in the Expert Choice software environment. Finally, by using the command to combine the layers based on the weighting of the AHP method in GIS, the final flood risk zoning map was obtained. NDWI water index was used to validate the flood risk map obtained.

The results of the AHP model showed that the most effective factors in the occurrence of flood risk in the Karun Watershed include rainfall, the amount of slope and the height classes, which should be considered in order to reduce flood damage and provide management solutions for these factors. Also, the results show that the downstream areas of the watershed have the highest risk of flooding and more than half of the watershed's surface (52.24%) has a medium flood potential.

Preparing a map of flood-prone areas is one of the most constructive methods that enable the reduction of flood risk damages and help planners, stakeholders and decision-makers to properly monitor flood-prone areas and ensure appropriate and sustainable socio-economic development.

In the mountainous regions of Iran, a significant part of the precipitation is in the form of snow, which is considered an important source of river flow. Accurate knowledge of the quantity of these resource is necessary in terms of the ever-increasing value of fresh water and also in terms of the optimal use of water resources. From a global point of view, snow monitoring and accurate information on the spatial distribution of snow cover, are necessary for weather forecasting and hydrological and meteorological modeling. An important feature of mountainous regions is the snow cover, which has a high reflectivity, has a great influence on the local weather, reduces the net radiation at the surface and as a result, transfers energy. In addition to being an important factor for ecosystem development, snow cover is very important for human activities. Accurate estimation of the coverage level is considered as one of the central and fundamental operations in the field of water resources management, especially in areas where snowfall is a major part of precipitation. Revealing and determining different characteristics of snow and ice using remote sensing data, which is widely used in hydrology, has created a new method to obtain the required parameters of hydrology.

The Alborz Mountain range which is under study of the current research, separates the coastal plains of Mazandaran Province from the interior of Iran. The eastern half of Western Alborz and all of Central Alborz and a part of Eastern Alborz are within Mazandaran Province. In this way, along with other natural factors, certain geographical conditions have emerged. In this region, snow plays a key role in the hydrological cycle and hydroclimate, and a significant part of the total annual runoff in this region is the result of snowmelt. So that global warming affects the management of watersheds and the downstream water requirements of its sub-basins. First, MODIS sensor data was obtained daily with a spatial resolution of 500×500 meters from NASA's National Snow and Ice Database (NSIDC). The received images are related to the period of 2000-2018. To process the images, first pre-processing wacovered ars applied in the ENVI 5.3 software environment. The NDSI index was used to monitor the snowed area. Mann-Kendall test, Sen’s slope estimator, and Pettitt's homogeneity test were used to investigate the snow cover variation trend. Also, the seasonal and annual anomalies of snow cover, temperature and precipitation in the study area were investigated based on standard Z score.

The results of the Mann-Kendall test and the Sen’s slope estimator method in the northern slope of Central Alborz, show that the largest reduction of the snow covered area occurred in January and winter season, respectively, equal to 220.39 and 50.41 km2 each year. The results of Petit's homogeneity test, using the Change Point Analysis (CPA) method, in January 2010 for the snow-covered area and May 2014 and June 2010 for the monthly mean temperature, showed a climatic jump at 0.05 significant level. Also, the change point in the snow-covered area time series of January has been descending, but the change point in the mean temperature time series of May and June has been ascending. Comparing the snow cover conditions with the mean temperature and total precipitation conditions, shows that in most cases the negative anomalies of snow cover are consistent with the positive anomaly of temperature and the negative of precipitation. The obtained results are a warning about the climate change in this region, which is known as the phenomenon of global warming and meteorological drought. Surely, these changes have a direct effect on the reduction of water resources for the agricultural and drinking sectors.

In general, the analysis of the snow-covered area variations in January during the studied 19 years, shows that for an increase in the average temperature of 0.13°c, the snow-covered area in this month decreased by 220.39 km2  every year. Also, according to the results of Pettitt's homogeneity test in 2010 and 2014, it can be concluded that global warming and meteorological drought caused a sudden change in the snow-covered area and temperature in these years and months. The comparison of precipitation and temperature conditions with the snow cover condition showed that in most years, the negative anomaly of snow cover was simultaneous with the positive anomaly of temperature and the negative anomaly of precipitation. The greatest effect of temperature increase has been observed in spring. Therefore, with the increase in temperature and the change in climatic conditions, the winter precipitation that will turn into snow accumulation has decreased and can affect the runoff caused by these precipitations in the spring season. Since this region has the ability to receive snow from mid-autumn to early spring, information about the snow covered area in this region is essential for many hydrological, meteorological, and climatological applications, as well as hydroelectric power generation and flood forecasting.

The construction of check dams in the branches is one of the common methods of watershed management to control sedimentation, watercourse stability and reduce the flood hydrograph from the time of concentration and peak flow. In Iran, despite being 50 years old, in the wide implementation of this small-scale structure by the bodies affiliated to the country's Natural Resources and Watershed Management Organization as an executive body, a suitable quantitative and qualitative evaluation method has not been provided. In expressing the effectiveness of this structure, it is inevitable to simulate natural conditions in the presence and absence of this structure in hydrological and hydraulic models. Of course, field visits and measurement of the relevant parameters in the field are also considered to be primary measures in the verification of the simulation and the approximate expression of the effectiveness. The investigations showed that in many researches, the effect of correction dams on the runoff hydrograph has been considered. Simulation of the dams has been done hydrologically with changes in the slope of the waterway and basin time concentration or by using the method of routing in the reservoir that according to simplified hypotheses, estimates more than reality. Hydraulic simulation is more precise but has its own complexity and obstacles. Therefore, in this study, we tried to apply the effectiveness of improving check dams in runoff hydrograph by using both the accuracy of hydraulic simulation and the lack of complexity of hydrological relations. The effectiveness of check dams is computed by determining and applying coefficients in the waterway output hydrograph without improving check dams to obtain the waterway output hydrograph with check dams.

In this research, the effectiveness of successive check dams in reducing the output hydrograph of a triangular channel with three lengths of 1000, 2000 and 3000 meters in three longitudinal slopes of five, 10 and 15%, using the MIKE 11 hydrodynamic model, is considered. In this study, it is assumed that series check dams with a height of 2.5 meters will be constructed in each triangular canal, therefore, the number of check dams will vary from 20 to 180 based on their length and slope. In this study, the output hydrograph of the triangular channel was considered as the dependent variable, and the input hydrograph, channel length, and channel slope were considered as independent variables. Variations of  outflow hydrograph peak discharge were investigated under two scenarios. The first scenario for the condition where the channel is without improving check dams and the second scenario for the case where the channel was studied with full of sediment series check dams in order to simulate the effectiveness of the dams in a waterway with hydrological parameters. Two criteria were defined to express effectiveness: the percentage of the intensity of hydrograph routing and the percentage of flow discharge change. The percentage of changes in peak discharge of the hydrograph is determined in relation to the peak discharge of the inlet hydrograph. In other words, "attenuation coefficient" was named based on the difference between inlet and outlet discharge for the scenario and for changes in length of waterway, slope and different amounts of inlet hydrograph. The percentage of change in peak flow discharge from the second scenario compared to the first scenario was also considered as the percentage of flow discharge change.

Evaluation of the model results for hydrograph routing along the channel in exchange for changing independent variables in the form of two scenarios resulted in decreasing peak flow, increasing the base time of output hydrograph, and delayed time due to trending. The existence of check dams has doubled the change in the mentioned parameters. As the longitudinal slope of the waterway increases, the amount of storage in the canal decreases, and the output discharge and therefore the intensity of the routing (decrease in peak outflow relative to the inlet). Increasing the volume of inflow decreases the intensity of the routing. Routing intensity has an inverse relationship with longitudinal slope and has a direct relationship with channel length. Increasing the number of check dams increases the amount of storage in the canal and as a result, slope reduction occurs and the changes in output discharge are greater than inlet flow. Therefore, the intensity of routing increases. The main purpose of this study was to determine the effectiveness of improving check dams in reducing peak discharge of ouflow hydrograph from a triangular channel based on different conditions using a mathematical model. After performing various simulations and investigating different methods, it was observed that the effects of improving check dams on a outflow hydrougraph can be modeled as the effect of a linear reservoir with a lag time at the end of the channel. In other words, two linear reservoir function and a lag time function are applied to independent variables to obtain the dependent variable. For both output hydrographs obtained in the channel without and with improving dams, K values were estimated as linear reservoir function and TL as  lag time function. The average storage coefficient (K) of the linear reservoir was estimated 500, 1100 and 1400 seconds respectively for lengths of 1000, 2000, and 3000 m and for three slopes. The mean lag time for the three mentioned lengths was 540, 1750, and 3700 seconds, respectively. As the length of the channel increases, the slope of the canal, as well as the inflow to the canal, as well as the inflow to the canal decreases, and the amount of the above parameters and therefore the attenuation coefficient increases.

If a stream is selected for the construction of improving check dams and the output hydrograph is available using empirical, hydraulic, and hydrological models in the absence of check dams, the outflow hydrograph from the stream will be simulated and modified for the existence of small-scale structures by applying the linear reservoir storage coefficients and the lag time obtained from this research. In this way, the effectiveness of the construction of improving check dams in flood control will be achieved in the mentioned waterway.

Accelerated soil erosion by water is an environmental threat on different continents. Suspended sediment loads in riverine systems resulting from the accelerated erosion due to human activities are a serious threat to the sustainable management of watersheds and ecosystem services therein worldwide. Identifying sediment provenance in the catchments is essential to to mitigate its negative effects consisting of on-site (e.g., decreasing soil depth and depletion of soil organic, degradation of soil structure, and etc.) and off-site effects and to help remedy problems such as eutrophication, and siltation of reservoirs. Among direct and indirect methods used to study the sediment source, sediment fingerprinting is a useful technique for determining contribution of sediment sources within a catchment like agricultural lands, rangelands, barelands, and etc. The successful application of this method reported in fluvial and aeolian environments. In this study, sediment fingerprinting method used to identify sediment sources and quantifying contribution of its sources in the Farghan Catchment in Hormozgan Province.

In this research, 38 surficial samples (0-5 cm) were collected randomly-systematic with a good distribution from the potential sources (consisting of eight samples in agricultural lands, 18 samples from gully erosion sites and 12 samples from barelands and rangelands) and six samples from sediment deposited in the bed of the river in vicinity of catchment outlet, respectively, and after samples preparation, the concentration of the geochemical elements (consisting of major elements, rare earth elements and trace elements) were measured by ICP-OES in the central laboratory of University of Hormozgan. Stepwise discriminant function (DFA) was applied to discriminate the sediment sources, and five tracers consisting of Te, Zr, Ta, Be and Na were selected as the final tracers. Finally, the relative contribution from each source was determined by mixing model.

Based on the results, the mean contribution for the agricultural lands, barelands and rangelands, and gully erosion sited were estimated 16.7, 50.6, and 32.7 %, respectively. Based on the results, a combination of Te, Zr, Ta, Be and Na were able to correctly classify 89.3% of the source sediment samples consisting of agricultural lands, gully erosion sites, barelands and rangelands. Due to high sediment rate, gully erosion sites are one of the important forms of soil erosion by water. The central parts of catchment are the most susceptible region to gully erosion because these areas are covered by lithological formations such as Bangestan, Aghajari and Mishan. Mishan lithological formation is involving the marl, limestone, and the Aghajari outcrop consists of sandstone and marl. The lands of flat plains are covered by quaternary fluvial depositions resulting from the erosion of Aghajari, Mishan and older lithological formations. Due to low slop of central parts of study area, existing young soils and without developed horizons and mismanagement of land uses, the land susceptibility to gully erosion is high in central parts.

Sediment source fingerprinting is a useful technique to investigate the origin of sediment in both windy and fluvial sedimentary environments. The estimated source proportions can help watershed engineers plan the targeting of conservation programmes for soil and water resources and due to the variability of geological units from one region to another, the type of land use management, and the type of soil units of each region, the selected trackers for each region are different, and for this reason, until now researchers are able to provide a comprehensive guide for choosing a tracker. were not optimal in all regions, and this issue is one of the main challenges of sediment fingerprinting.

In recent years, factors such as the growth of industrial activities and environmental destruction have led to an increase in greenhouse gases, resulting in disruption of the climate balance known as climate change. The negative impact of this phenomenon on various systems, such as water resources, agriculture and industry, has raised concerns in human society. Consequently, addressing the issue of climate change regarding water resources has become one of the primary causes of concern today. Climate change and its effects pose significant challenges to water and energy resource management, necessitating thorough investigation and developing plans to mitigate its impact on water resources. This study aims to identify the region's most suitable climate change model and assess the effectiveness of artificial intelligence methods in studying the climate change phenomenon.

One of the most reliable approaches for studying the parameters influencing hydrological phenomena under climate change is atmospheric general circulation models. To employ these models on a regional scale, downscaling operations are necessary. Given the large number of parameters derived from Earth's General Circulation Models (GCMs), selecting the most influential parameters is essential before proceeding with the exponential downscaling process. In this study, the meteorological and hydrological parameters of the Ardabil synoptic station were determined using 25 models from the fifth series of the IPCC report. The linear correlation coefficient between monthly precipitation and observed temperature with the output of GCM was used to identify the most appropriate model among the reviewed models. Artificial Neural Network (ANN) was also utilized to downscale the GCMs output. Before employing the neural network, the linear correlation coefficient, the standard information function, and the M5 decision tree were used to identify the most suitable input parameters from the parameters of the best GCMs in the region, to obtain an ideal and optimal network.

This research investigated 25 models from the fifth series of the IPCC report to explore the uncertainty of GCMs. The results indicated that three models-MRI-CGCM3, CMCC-CMS, and MPI-ESMMR-demonstrated the most suitable correlation coefficients at the Ardabil synoptic station. The findings related to determining the most appropriate input parameters for exponential downscaling, using three methods linear correlation coefficient, standard information function, and M5 decision tree, revealed that the decision tree algorithm provided the most suitable parameters. Moreover, the results obtained from the downscale analysis using the neural network with the variables selected by the decision tree method exhibited the excellent performance of this approach in selecting the effective input parameters of the neural network. Specifically, using the selected parameters of the MRI-CGCM3 model as input for the neural network as a downscaling method yielded better outcomes. The results obtained using the selected parameters of the MRI-CGCM3 model indicated that for the precipitation parameter, the values of the Determination Coefficient (DC), Root Mean Square Error (RMSE), and Correlation Coefficient (CC) for the test data were 0.39, 0.04, and 0.63, respectively. For the temperature parameter, the values of DC, RMSE, and CC for the test data of the superior model were 0.9, 0.03, and 0.95, respectively.

The performance of exponential downscaling networks is determined by the climatic conditions of the region. The superiority of a particular model in one study cannot be regarded as a valid argument for selecting that model for all regions. It is advisable to utilize different models of the general earth circulation within the region to identify an optimal model. Conducting such studies can assist researchers in investigating various hydrological phenomena that may occur in the future, which may have irreparable consequences.

Evaluating the implemented watershed projects and providing a perspective of their performance results provide managers and decision-makers with appropriate information for long-term planning. Therefore, by evaluating the performance of watershed projects from the perspective of experts, while determining the effects of the project and the factors affecting, the necessary guidelines for the optimal implementation of these projects in the future can be provided to officials and planners. In this research, in order to evaluate the implemented watershed management projects in terms of improving the spirit of public participation in the Rimeleh Watershed, the point of view of experts and users was used.

Rimleh Watershed is one of the sub-basins of Kashkan River. For this purpose, after preparing the initial list of indicators, using the Delphi method and polling experts, the final list of indicators effective in improving the spirit of public participation was determined. 39 indicators were selected and classified into 6 categories. Finally, in order to prioritize the indicators, multi-indicator decision-making methods were used. All heads of rural households in this watershed were selected as the statistical population. The validity and reliability of the questionnaires were tested. According to Cochran's relationship and proportional assignment sampling method, 135 watershed dwellers were referred from the watershed dweller community. Face-to-face interviews and questionnaires were used to collect users' opinions. Friedman's test was used to rank the factors affecting people's participation from the point of view of beneficiaries. In order to evaluate from the point of view of experts, a pairwise comparison questionnaire was designed and distributed using the Analytic Hierarchy Process (AHP) method.

Cronbach's alpha was calculated equal to 0.827 and showed the reliability of the questionnaires in the research. The compatibility rate in hierarchical analysis is less than 0.01, so the compatibility of the comparisons can be accepted. The results show that the activities of cooperative actions such as gardening, terracing, construction of concrete streams, construction of swimming pools and dredging of fountains took the most human and financial participation (about 76%). Examining indicators of participation of watershed residents in future projects showed that "increasing the level of income due to the implementation of the project in the region" (ranked 19.5) was chosen as the best indicator from the users' point of view. According to experts, the index "participation rate of watershed residents in decision-making" (with a weighted average of 0.252) was selected as the best index. The indicators of "increasing the participation of watershed residents in decision-making" and "using the capacity of other institutions in the implementation of conservation projects and attracting their participation" received the most weight, and the indicator "strengthening the participation of organizations such as cooperatives at the village level" received the least weight.

The results of this research show that the beneficiaries of Rimeleh Watershed have a high capacity to accept and participate in watershed management projects. It is suggested that the participation of stakeholders in future decisions and the use of participatory measures in this watershed and other watersheds are of importance. Therefore, the approach used in this research can be used as an effective method to help better understand the watershed system and also facilitate the decision-making process by watershed planners and managers and watershed stockholders. In order to evaluate the implementation of watershed projects on improving the spirit of public participation, it seems necessary to pay attention to the mentioned priorities and it is suggested to be placed on the agenda of managers and planners.

The requirement of a comprehensive water law is the drafting and revision of laws and the scientific and legal regulation of water distribution laws. The idea of a comprehensive law is consistent in many countries to eliminate the fragmentation of laws. The comprehensive water law means the thematic examination of the laws and legal rules related to water, the realization of which requires spending a lot of time. Lack of water resources and drought, increase in population and excessive consumption of water, the existence of vague and unenforceable laws in the water sector are among the most important challenges of the country, and for the fair allocation and implementation of the correct management of the protection of water resources in watersheds, it is necessary to formulate a comprehensive water law. Failure to examine the legal position of environmental rights, as the cause of destruction of wetlands in Iran on the one hand and the indiscriminate use of river water on the other hand, causes various changes in the water flow regime and destruction of the river ecosystem and It has become wetlands and it shows the importance of drafting a comprehensive water law in Iran. The results of clarifying and drafting a comprehensive law will lead to an increase in social security and public welfare. Failure to pay attention to this type of research will lead to an increase in injustice, destruction of water and soil resources, loss of national rights and rights of people, and destruction of the environment. The purpose of the research is to examine the water laws in Iran in order to solve legal challenges and provide the basis for the development of the country's comprehensive water law.

Research materials and methods include collecting statistics and information on water laws and identifying and separating the main and secondary laws related to water, valid ruling laws from non-valid ruling and obsolete laws, and examining the thematic, conceptual relationship and analysis of water laws from a legal and structural point of view. An organization that includes the statistical population of all laws enacted during the legislative period from 1906 to 2021. In this research, a list of the main and secondary water laws, including valid and obsolete laws and expired, rejected and conflicting laws, was prepared in the form of tables and histograms, and based on the reference, ID number and date of approval, it was collected and analyzed from library and internet sources. It was conducted qualitatively in the form of theoretical framework, content analysis and conceptual model, and the parameters of the number of obsolete, conflicting, expired, repealed and valid ruling laws were quantitatively analyzed.

The research results show that out of 364 water-related laws, only 6.6% are in the form of statutes and about 21.7% are valid laws. 30% are related to protocols, agreements, conventions and contracts. 6.6 percent of the laws of the water sector are explicitly and implicitly repealed, 6% are expired and 29.1% of the existing laws are repealed, which can often be repealed during the revision process and will provide the basis for the development of the country's comprehensive water law.

The analysis of the questionnaire also shows that 95% of the respondents by choosing a very high option and 5% by choosing a high option consider it necessary to revise the comprehensive water law. Among the respondents, 89% believe that the drafting of the comprehensive water law has an effect on attracting people's participation in preserving water resources and watershed management. Therefore, the answer to most of the questions is affirming the necessity of developing a comprehensive water law.

Landslides are one of the natural hazards in mountainous areas that threaten the safety of residents and the environment. In the past few decades, landslides have caused damage to natural and human resources in the Saqezchi Basin in the south of Ardabil Province. Landslides have occurred in more than 9.2% (2600 ha) of the area. In this basin, like other landslide areas, for land-use planning and management, it is necessary to analyze the whole area in order to estimate the probability of landslides occurrence in the future. It is possible to solve this problem by analyzing the geomorphology, topography, geology, land use, hydrology and climate factors of the basin in the form of information layers in the geographic information systems on a regional scale. Landslide susceptibility assessment has not been done with modern methods and with high accuracy in the Saqzachai Basin until now. The results of this research can be used in predicting the possible occurrence of landslides and reducing damage in the Saqzachai Basin. 

The research basin with an area of 27,918 ha is located in the south of Ardabil Province and in the southwest of  Khalkhal City. In this basin, the inventory map was generated based on 113 landslides, the training dataset and validation dataset were, respectively, prepared using 70% landslides and the remaining 30% landslides. Ten landslide causative factors based on slope angle, slope aspect, distance to faults, distance to stream network, distance to the roads, distance to settlement area, lithology, land-use, peak ground acceleration (PGA) and average annual precipitation were applied for the models analysis. Two nonlinear methods of neural network called multi-layer perceptron with feed forward structure and logistic regression were used to predicting the susceptibility of landslide occurrence. The probability of landslide occurrence in each pixel was calculated based on both models. The prediction accuracy of the two models were evaluated using the Receiver Operating Characteristic (ROC) curve.

In the neural network model, landslides triggering factors, including the average annual precipitation (0.136) and the peak ground acceleration (0.134), have been the greatest effect in predicting the probability of landslides. The factors of slope angle (0.067), slope aspect (0.069), distance to faults (0.110), distance to stream network (0.101), distance to the roads (0.109), distance to settlement area (0.096), lithology (0.109) and land-use (0.068) are respectively important in landslides susceptibility modeling to using artificial neural networks. Therefore, all ten factors were used in modeling by artificial neural networks. The results indicated that the probability of landslide occurrence varies from 0.00 to 0.961. In the classification of the watershed according to the degree of landslide susceptibility by the natural breaks method based on the estimated probability by the neural network method, 85.7% of the area is placed in the zones with low and very low susceptibility. In 6.6% of the area, there is a probability of moderate susceptibility, and in 7.7%, there is a high and very high landslide susceptibility. Landslide susceptibility analysis is started without independent variable and ended by adding variables in the tenth step using logistic regression method. The results show that only three levels of the factor of slope aspect are ineffective in the logistic regression model. Probability values were calculated between 0 and 1 for all pixels in the area based on the values of independent variables by estimating constant and coefficients related to logistic regression model. The landslide-prone areas of low and very low susceptibility, medium susceptibility and high to extremely high-susceptibility grades are 79.9%, 10.1%, and 10%, of area, respectively, by the natural breaks method in the logistic regression model. The accuracy and validity of the logistic regression and artificial neural network models based on the ROC curve and the area under it (AUC) are equal to 0.848 and 0.929, respectively. The findings of the models show good results with the accuracy of two models being higher than 84%. The results obtained from two methods in most studies in the world and in Iran indicate their ability to accurately estimate susceptibility to landslides occurrence, but the artificial neural network method is more accurate despite its specific complexities.

Landslides are an important limitation for development in the landslide areas in the south of Ardabil Province. The environmental conditions in the Saqzachi Basin are susceptible to the occurrence of new landslides or the reactivation of old landslides. The probability of landslides occurrence was simulated using effective factors and using logistic regression and artificial neural network models in the region. The results obtained from the artificial neural network model are the most accurate and better than the logistic regression model. The landslides triggering factors, including the average annual precipitation and the peak ground acceleration have the greatest impact to predicting the probability of landslide occurrence using the artificial neural network model. The findings of the models show good results with the accuracy of two models being higher than 84%. The artificial neural network method is superior in explaining the relationship between landslide occurrence and influencing factors. The landslide susceptibility map was prepared using this method by dividing into five class, namely: very low (71.4%), low (14.3%), moderate (6.6%), high (4.3%) and very high (3.4%) susceptibility zones. Therefore, it is recommended to use the artificial neural network models in landslide susceptibility assessment in the basin and similar regions to help decision makers, planners, land use managers and government agencies in hazard and damage reduction.