حسین حکیمی خانسر

Estimation of air temperature is importan in environmental and agricultural sciences. The aim of this study is prediction of daily air temperature (mean, maximum and minimum) using several types of AI models, in Rasht station north of Iran. According to the results, the adaptive neural-fuzzy inference system, artificial neural network, and gene expression programming were ranked first, second and third, respectively, despite of a slight difference in prediction accuracy of the selected models. Besides, the developed mathematical equation between the input and output variables using the gene expression programming model showed the superiority of this approach to the other two models. Based on the SI index for the minimum and mean daily temperature in training period is varied in range of 0.1 to 0.2, i.e acceptable in terms of model accuracy. For the maximum temperature it ranged 0.2 to 0.3 which is considered as average accuracy. The findings revelead the best performance can be obtained using inputs in one to three days lead time.

Estimating the water inflows to water resources systems is one of the essential measures for awareness of planning and allocating optimal water resources in different sectors of consumption. In this study, a combination of Meta Heuristic algorithms including the Water Cycle Algorithms (WCA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Neural Network Algorithm (NNA) and Grasshopper Optimization Algorithm (GOA) for Neural-Fuzzy System training and update parameters was used. Finally, the best models were developed to predict the daily inflow of Sefidrood reservoir dam. This method does not suffer from the problems of training gradient-based algorithms. Four features including dam lake area, reservoir volume and reservoir level of the dam in the 7 lags and inlet flow in the 1 lag were selected according to the AutocVarious statistical indicators were used to evaluate the performance of utilized models. In the test stage, ANFIS-WCA model presents the lowest values of SI, MAE and NRMSE equal to 0.0736, 0.05048 and 0.0736, respectively, and the maximum value of R^2equals to 0.9840, which indicates its superiority over other models. Based on GPI index, ANFIS-WCA model was selected as the best model and then ANFIS-NNA, ANFIS-GOA and ANFIS-WOA models were ranked, while the worst accuracy was obtained through the ANFIS-GOA model. The high accuracy of the ANFIS-WCA model compared to other hybrid models indicates the performance of the WCA for escaping local optimization in combination with the ANFIS model which has enabled this algorithm as a powerful tool for estimating the inflow of Sefidrood dam.

Pore water pressure, stress and settlement are the most important geotechnical parameters that must be constantly monitored during the construction of earth dams. Since measuring dam settlement directly at the time of dam construction requires cost and time, the development of artificial intelligence methods can be very effective. Most studies have been done in the field of modeling earth dams during construction with a numerical model; therefore the need for artificial intelligence modeling in this field seems to be necessary. Artificial intelligence models, including neural networks, are used for study and modeling many engineering sciences. Also, with the development of meta-heuristic algorithms, their combination with neural networks has become very wide-spreading due to more accurate results. So, The purpose of this study is to determine the most effective features in modeling settlement in the body of earthen dams at the time of construction as a case study (Kaboud-val dam) using the hybrid algorithm Dragonfly - artificial neural network in different points of the body of earthen dam at the time of construction. Therefore, in this research, new inputs in artificial intelligence modeling have been proposed for this purpose and their importance in different levels of installation has been investigated.

Kaboud-val Dam is located in Golestan province of Gorgan and around the city of Aliabad. This dam is homogeneous and has a filter and inclined drainage. In order to obtain the deformations of the body and the foundation of Kaboud-val dam, settlement plates have been installed in different sections of the body and its foundation during the construction. In this study, instrumental data related to the section of 19 Kaboud-val dam were used. Also, out of 17 pages, 4 pages named M1, M5, M9 and M13 (installed in the body and Kabudwal dam at levels 140, 152, 164 and 180 meter, respectively) have been used for modeling. By analyzing the data of section 19 pages, fill level (F), reservoir level (RL), dam construction time (T), fill rate (FR) and impounding rate (RV) for inlet and landing (P) on terms of (kp), was selected as the output of the hybrid model in the feature selection method. In this study, in order to select the best combination of input features in the artificial neural network, the dragonfly algorithm was used. Feature selection is a method of selecting a subset of related attributes (the best combination of them) that is relevant to a particular goal. The most important principle is to choose stable features and remove extra data. The combination of dragonfly algorithm with artificial neural network as DA-ANN is shown, Therefore, the dragonfly algorithm (DA) models a variety of different combinations of features with an artificial neural network and selects the best least error combination (RMSE) as the optimal artificial neural network model.

By performing a hybrid algorithm, sensitivity analysis and feature selection method, combining the four features on pages M1, M5,M9 and M13 with error values (RMSE) of 0.0023(kPa), 0.0024(kPa), 0.0026(kPa), respectively, and combining the three features on the M13 page with the value Error (RMSE) equal to 0.0035(kPa) was the best input combination. The three features of construction time, fill level and reservoir level as common features in all plates are the most effective features in modeling the settlement on selected plates. On plates mounted at higher levels, the modeling error increases, because during the test period and for plate M13 (with the highest mounting level), according to the statistical indices R^2, SI, NSE and NRMSE are equal to the values of 0.9998, 0.0062, 0.9998 and 0.0062 respectively, have poorer performance than other pages. The effect of reservoir level feature on the plates installed at higher levels due to the high sensitivity coefficient is more than other points and the fill level feature has the least effect on subsidence modeling.

The results are very important considering the cost of installing the measuring equipment and the significance of estimating the actual values in the future. The present study shows that the DA-ANN hybrid model is an important tool in predicting and selecting the best input combination for the intelligent model of the target variable of the settlement at the time of construction of earth dams. However, assessment of this model using the input data studied in different dams is necessary to ensure the application of these models in different conditions.

Accurate prediction of pore water pressure in the body of earth dams during construction is one of the most important factors in managing the stability of earth dams. In this study, using three different evolutionary neural network models including multilayer perceptron neural network with genetic algorithm, particle swarm optimization and Imperialist Competitive algorithm for estimating the pore water pressure in the body of Kabudwal earth dam at the time of construction, has been studied. Five features including fill level, construction time, reservoir level, impounding rate and fill speed during the 4-year statistical period were selected as the input of the hybrid model in piezometer EP19.7. The composition of the inputs was obtained using the feature selection method and the hybrid water cycle algorithm -artificial neural network. By performing hybrid algorithm and sensitivity analysis and feature selection method, fill level, construction time, reservoir level and dewatering speed were selected as the top four inputs, because the combination of these four features with MSE value of 1.1587 had the least error. In this study, artificial neural network weights are optimized to increase efficiency using the above three meta-heuristic algorithms. In general, according to statistical indicators, the results indicate acceptable accuracy of all three hybrid models. In terms of priority, the ANN-GA hybrid model with the highest accuracy and minimum error and values of , RMSE and MAE are equal to 0.9773, 0.0457 and 0.0399, respectively, is first priority and ANN-PSO and ANN-ICA hybrid models were given the next priorities, respectively.

Accurate prediction of pore water pressure in the body of earth dams during construction with accurate methods is one of the most important components in managing the stability of earth dams. The main objective of this research is to develop hybrid models based on fuzzy neural inference systems and meta-heuristic optimization algorithms. In this regard, the fuzzy neural inference system and optimizing meta-heuristic algorithms including genetic algorithms (GA), particle swarm optimization algorithm (PSO), differential evolution algorithm (DE), ant colony optimization algorithm (ACOR), harmony search algorithm (HS), imperialist competitive algorithm (ICA), firefly algorithm (FA), and grey wolf optimizer algorithm (GWO) were used to improve training system. Three features including fill level, dam construction time, and reservoir level (dewatering) obtained from the dam instrumentation were selected as the inputs of hybrid models. The results showed that the hybrid model of the genetic algorithm in the test period had the best performance compared to other optimization algorithms with values of R2, RMSE, NRMSE, and MAE equal to 0.9540, 0.0866, 0.1232, and 0.0345, respectively. Also, ANFIS-GA, ANFIS-PSO, ANFIS-ICA, and ANFIS-HS hybrid algorithms performed better than ANFIS-GWO, ANFIS-FA, ANFIS-ACORE, and ANFIS-DE in improving ANFIS network training and predicting pore water pressure in the body earthen dams at the time of construction.

The stress created in the soil significantly affects its engineering behavior. Changing its value during the construction of earthen dams causes volume change and shear strength, causing rupture, soil compaction and settlement in earthen dams. So measuring soil stresses of dams is essential that it is done by instrumentation installed. Artificial intelligence models such as artificial neural networks for modeling many engineering applications. Also by extending the meta-heuristic algorithms, combined with neural networks have become very popular due to more accurate results.

In this study, the cross-section 19 (cross-section of the middle part of the body and dam foundation) for the modeling of soil stress were used during the construction of the dam Kaboud-val. Also Kaboud-val dam instrumentation data (derived from Golestan Regional Water Co.) was used at the time of construction during the period of 4 years. Type and number of input data is the most important thing in modeling artificial intelligence. By examining data TPC19.1 cells in section 19 in Kaboud-val dam, embankment alignment (F), the water level of the reservoir (R), the construction of the dam (T), speed filling and dewatering speed was selected for input. The soil stress (P) in the body of the dam during construction, intelligent model was selected for output. This process is most effective in a subset of features from the set of input features according to the least error, selected and additional features will be removed. In this research, a meta-algorithm (artificial neural network (NNA) algorithm) is combined with an artificial neural network (ANN) that has the ability to predict complex and nonlinear relationships and extracts effective features for modeling soil stress with appropriate accuracy. In this study, the most effective features in soil stress modeling were determined in a case study (Kaboud-val Dam) using the NNA-ANN hybrid algorithm and a comparison was made between the results of the hybrid model and the numerical model. Five features include reservoir level, fill level, dam construction time, impounding velocity and fill velocity was selected for the input.

Using hybrid algorithm and feature selection method, a combination of three features, including reservoir level, fill level, dam construction time (with RMSE equal to 0.5024) were the most effective features in modeling soil stress in the selected cell. The results showed that the hybrid model in Kaboud-val Dam (with values of R^2, RMSE, MAE and NS equal to 0.9943, 2.5653, 1.9973 and 0.9999, respectively) has better performance in modeling soil stress than the numerical model. (With values of R^2, RMSE, MAE and NS are equal to 0.9625, 26.2567, 16.6725 and 0.9772, respectively). The results showed that the reduction in the input features to reduce the time and cost reduction is more economical and more effective. Because with the increase in the number of features in the hybrid model, the increase in modeling accuracy did not occur. Sensitivity analysis showed that the dam construction time and fill level, of the highest sensitivity factor, the most important feature of the model is the total stress in cells. Modeling with the mentioned features, in another dam with a new construction site and new geotechnical specifications (Masjed Soleiman Dam) showed that the use of artificial intelligence model according to statistical indicators has more accurate answers than the numerical model.

The results showed that the use of artificial intelligence methods in the design and initial estimates of soil stress parameters in earthen dams instead of using numerical methods has high reliability and accuracy. The combination of input data in the hybrid model under study is suitable for Kaboud-val dam and Masjed Soleiman dam and the appropriate combination should be used for each construction site. By completing the number of data in different sections of the dam and the number of construction sites in areas with similar climate and geotechnical conditions, a design software can be obtained to predict the amount of soil stress during construction in the body and foundations of earth dams.

The general purpose of this paper is to select effective features and model soil stress in earth dams at the time of construction with the neural network using an optimization algorithm and then compare the results of the artificial neural network model with usual ANFIS and GEP methods. Five features including fill level, dam construction time, reservoir level (dewatering), dewatering speed and embankment speed were selected as hybrid model inputs. By performing hybrid algorithm and sensitivity analysis and feature selection method, fill level and dam construction time, the most effective features were in modeling the total stress in selected cells, because the dual composition including fill level and construction time in TPC25.1, TPC25.3 and TPC25.4 cells, The error values (MSE) of 1.523, 2.747 and 0.750 were the most effective features in these cells, respectively. In TPC25.2 cell, the selection of three features including fill level, construction time and dewatering level according to the error (MSE) value of 5.245, has the greatest effect in modeling the total soil stress in this cell. Comparison between ANN model with ANFIS and GEP showed that although the difference in the accuracy of the models is very small, it can be said that all three models had acceptable answers. The results also show that the higher the dispersion of the model input data, the more the ANFIS model has the ability to simulate than the two models ANN and GEP,

One of the basic measures in managing the stability of earth dams is to accurately estimate the amount of pore water pressure in the body of the dam during and after its construction. In this study, three different models of artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and gene expression programming (GEP) to estimate the pore water pressure in the body of Kabudwal earthen dams at the time of construction have been studied and compared. Five features including fill level, construction time, reservoir level, impounding rate and fill speed have been used during the 4-year statistical period as input of models in 4 piezometers installed in the dam body. The first three features were the most effective inputs according to the cross-correlation function. In this study, the results obtained from artificial neural network (ANN) in two piezometers according to statistical indicators, provided more accurate answers than gene expression programming (GEP) and ANFIS, but in the other two piezometers it was the opposite. Also, ANFIS and GEP models provided more accurate answers in piezometers that had higher data scatter than ANN model. Finally, based on the GEP model, mathematical relationships between input features and output variables were extracted.

In this study, the ability of water cycle algorithm in combination with ANN to model the volume of water delivered to the Irrigation and drainage network and determine the effective characteristics, was investigated. For the input of artificial intelligence models, five Features include the volume of water delivered to Sefidrood Irrigation and drainage network for one lags, flow and inlet volume for seven lags, level and reservoir volume for ten lags, were chosen. Using a hybrid algorithm, feature selection method and sensitivity analysis, a triple combination of features (with MSE of 0.00045) is the best input combination. The volume of water delivered to Sefidrood Irrigation and drainage network for one lags according to sensitivity analysis has been the most effective feature in its modeling. Then the artificial neural network weights were optimized to increase efficiency and with the help of two meta-innovative algorithms: water cycle without evaporation (WCA) and water cycle with evaporation (ER.WCA). The ANN-ER.WCA hybrid model with the highest accuracy (with values of R, NRMSE, MAE and NS equal to 0.9915, 0.0975, 0.0090 and 0.9829 in the test period, respectively) is in the first priority and the ANN-WCA and ANN models are next in line, respectively.

Accurate prediction of pore water pressure, settlement, soil stress and pore water pressure coefficient (Ru) in the body of earth dams during construction is one of the necessary measures in the management of earth dam stability. In the present study, which is a case study, a three-dimensional numerical simulation was performed using the Plaxis software for the Kabudval Dam located in Golestan province, Iran. The values obtained from the numerical simulation were compared with the corresponding measured values using the dam instruments. Calibration was carried out using back analysis method (BAM) and some dam geotechnical parameters were corrected based on BAM. The results showed that the hardening soil (HS) model with the statistical indicators of R2, RMSE and GMER is more accurate compared with the Mohr- Coulomb (MC) model. The results of the numerical model were calibrated at the end of construction for Kabudval Dam and showed that the maximum increase in pore water pressure, stress, settlement and horizontal displacement occurs in the central part and its value in the axis and middle part of the dam is more than its sides. The middle part and close to the dam axis have similar changes with the fill process of the dam body; while with moving away from the dam axis due to the transfer of stresses to the sides, they have less impact from the dam filling process. In addition, in the central part, the effects of filter and drainage is low.

In this study, the ability of WCA-ANN hybrid algorithm to model the pore water pressure coefficient in the body of Kabudwal dam at the time of construction was investigated and the effective features were identified. Therefore, five features including fill level, time, reservoir level, dewatering rate and fill speed during the 4-year statistical period were selected as the input of the model. By running the hybrid algorithm and feature selection method, the two features of fill level and time at points RU19.1  and   RU19.2 have the greatest impact on modeling the pore water pressure coefficient. In addition to the above two features, in the points of the middle axis , the features of fill speed and reservoir level with error value (MSE) equal to 0.00006 and in points close to the dam reservoir, dewatering level and dewatering rate with error value equal to 0.00004 are effective in modeling the pore water pressure coefficient. The results showed that at points close to the dam axis, the fill level and at points farther from the middle axis construction time (with high sensitivity coefficient) was recognized as the most important features in modeling the pore water pressure coefficient with artificial intelligence models.

In this study, the effect of soil material parameters including soil specific weight (γ), cohesion (C) and angle of internal friction (∅) and geometric parameters of slope including angle with the horizontal (β) and slope height (H) on factor of safety (Fs) are investigated. Slope factor of safety is considered in two scenarios: (i) slope with dry condition and (ii) slope with steady-state saturated condition that comprises water level drawdown circumstances. In addition, the type of slip circle investigated. For this purpose, the limit of equilibrium method (LEM) is implemented. Results indicated that, decreasing of water level and omitting the hydrostatic pressure on the slope, would result in safety factor decrement such a way that with drawdown of 5.5 m water level, the factor of safety decreases about 41.42 % and also the type of slip circle is changed. Comparison of the plane and circular failure surfaces showed that plane failure method has good results for near-vertical slopes only. Determination of clip type showed that for β〖60〗^o only toe circle can happen. Application of the LEM in Bishop’s method resulted the values of R2 and RMSE equal to 0.93 and 0.121, respectively that the error of this method is 1.3% respect to other methods, which can be neglected in comparison with the complex and accurate methods.

Modeling of Kaboodval Dam using Plaxis software has been used for the Mouher-Columb behavior model. The effect of two continuities of embankment and watering operations on the meeting was considered. The body structure of the dam was increased from the side of the faces to the middle sections, and the maximum seating was recorded at 25-25 and at about 2200 mm. By examining at different intervals, the largest meeting was in the range of 180 to 185. That is, the level of the embankment was found to be critical in these numbers. Most concerns were regarding the middle of the dam, which had a weaker position. According to the analysis of different parts of Kaboudvall Dam, the materials forming the right wing of the dam in the middle and left wings of the dam were better. In the case of the 19th Module, besides the Mouher-Columb model, the dam could be modeled with hardening and hardening models. Here, the hardening model created a better fit. The hardening model, as it could get more data from the soil, is likely to better model the behavior of the soil dams. Due to the fact that, during the construction, the first sessions usually occur, the hardening model can have a better performance.