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Abstract The historical data on water intake into the reservoir is collected and used within the framework of a deterministic optimization method to determine the best operating parameters for the dam. The principles that have been used to extract the best values of the flow release from the dam may no longer be accurate in the coming years when the inflow to dams will be changing, and the results will differ greatly from what was predicted. This represents this method’s main drawback. The objective of this study is to provide a framework that can be used to guarantee that the dam is running as efficiently as possible in real time. Because of the way this structure is created, if the dam’s inflows change in the future, the optimization process does not need to be repeated. In this case, deep learning techniques may be used to restore the ideal values of the dam’s outflow in the shortest amount of time. This is achieved by accounting for the environment’s changing conditions. The water evaluation and planning system simulator model and the MOPSO multi-objective algorithm are combined in this study to derive the reservoir’s optimal flow release parameters. The most effective flow discharge will be made feasible as a result. The generalized structure of the group method of data handling (GSGMDH), which is predicated on the results of the MOPSO algorithm, is then used to build a new model. This model determines the downstream needs and ideal release values from the reservoir in real time by accounting for specific reservoir water budget factors, such as inflows and storage changes in the reservoir. Next, a comparison is drawn between this model’s performance and other machine learning techniques, such as ORELM and SAELM, among others. The results indicate that, when compared to the ORELM and SAELM models, the GSGMDH model performs best in the test stage when the RMSE, NRMSE, NASH, and R evaluation indices are taken into account. These indices have values of 1.08, 0.088, 0.969, and 0.972, in that order. It is therefore offered as the best model for figuring out the largest dam rule curve pattern in real time. The structure developed in this study can quickly provide the best operating rules in accordance with the new inflows to the dam by using the GSGMDH model. This is done in a way that makes it possible to manage the system optimally in real time.

The historical data on water intake into the reservoir is collected and used within the framework of a deterministic optimization method to determine the best operating parameters for the dam. The principles that have been used to extract the best values of the flow release from the dam may no longer be accurate in the coming years when the inflow to dams will be changing, and the results will differ greatly from what was predicted. This represents this method's main drawback. The objective of this study is to provide a framework that can be used to guarantee that the dam is running as efficiently as possible in real time. Because of the way this structure is created, if the dam's inflows change in the future, the optimization process does not need to be repeated. In this case, deep learning techniques may be used to restore the ideal values of the dam's outflow in the shortest amount of time. This is achieved by accounting for the environment's changing conditions. The water evaluation and planning system simulator model and the MOPSO multi-objective algorithm are combined in this study to derive the reservoir's optimal flow release parameters. The most effective flow discharge will be made feasible as a result. The generalized structure of the group method of data handling (GSGMDH), which is predicated on the results of the MOPSO algorithm, is then used to build a new model. This model determines the downstream needs and ideal release values from the reservoir in real time by accounting for specific reservoir water budget factors, such as inflows and storage changes in the reservoir. Next, a comparison is drawn between this model's performance and other machine learning techniques, such as ORELM and SAELM, among others. The results indicate that, when compared to the ORELM and SAELM models, the GSGMDH model performs best in the test stage when the RMSE, NRMSE, NASH, and R evaluation indices are taken into account. These indices have values of 1.08, 0.088, 0.969, and 0.972, in that order. It is therefore offered as the best model for figuring out the largest dam rule curve pattern in real time. The structure developed in this study can quickly provide the best operating rules in accordance with the new inflows to the dam by using the GSGMDH model. This is done in a way that makes it possible to manage the system optimally in real time. [ABSTRACT FROM AUTHOR]

The fluctuation of groundwater level is one of the important criteria required for decision-making in many water resources exploitation models. The lack of reliable and complete data is one of the most important challenges in analyzing the decline and predictions of the groundwater level in water management. In recent years, the use of different numerical models has been noticed as a reliable solution. These models are able to estimate based on extensive statistics and information and based on various land maps and measurements such as pumping tests, geophysics, soil and land use maps, topography and slope data, different boundary conditions and using complex equations. The level of groundwater in any region. In the present research, first, by using available statistics and information and maps, the fluctuations of the groundwater level of Sonqor Plain were simulated by the GMS model, and the accuracy of the model was evaluated in two stages of calibration and validation. Then, due to the need for much less data in machine learning methods, GWO-ANN and PSO-ANN hybrid methods and LSTM and SAELM models were used. The results showed that the output of the SAELM model had the best fit with the observational data with a correlation coefficient equal to 0.97, and it also had the best and closest distribution of points around the 45 degree line, and in this sense, it is considered the most accurate model. Therefore, to predict the level of groundwater in the whole plain, instead of using the complex GMS model with a very large volume of data and also a very time-consuming calibration and validation process, SAELM model can be used with confidence. This approach greatly helps the researchers of the groundwater sector to predict the changes of the groundwater level in dry and wet years without using numerical models with a complex and time-consuming structure using artificial intelligence with high accuracy. [ABSTRACT FROM AUTHOR]

Background and Objectives Artificial intelligence models as powerful methods in modeling complex nonlinear problems have a significant ability, and this has been proven in numerous articles. Artificial intelligence has been used in various issues, including engineering, medicine, etc. The success of these methods compared to analytical and numerical methods, their easiness, speed, and accuracy. Today, one of the challenges of human life is the issues related to water resources management. This study has investigated the performance of artificial intelligence and regression models in water resources problems. Various researches have been done in the case of modeling and parametric analysis of water resources. However, this study used artificial intelligence (Learning Machine) models to simulate water's qualitative and quantitative parameters. The models used in this study are SelfAdapting Extreme Learning Machine (SAELM), Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). Due to the growing global population, one of the most critical challenges is access to safe drinking water. Due to its location in the semi-arid region and low rainfall in our country, Iran, this danger is felt more than ever. One of the serious issues is the salinity leakage into groundwater resources. In this study, an attempt has been made to simulate the leakage of salinity dynamic flow into the freshwater resources of the coastal aquifer, using artificial intelligence and statistical models. Methodology The study area in this work is Mighan Wetland in Markazi province. Annual rainfall occurs in small amounts in this area. According to the statistical results, the maximum and minimum rainfall values range from 461 mm in the northeast to 208 mm in the center of the Arak plain. In this study, qualitative and quantitative parameters: water salinity, total dissolved solids (TDS), chloride ion (Cl), sampling time (t), electrical conductivity (EC), salinity, and groundwater level (GWL) were simulated. This work used ANFIS, LSSVM, SAELM, and MLR models for simulation. In this study, data from 173 months of sampling were used. 80% of the sample size was used for training and 20% for testing models. After simulation and obtaining the results, the Wilson Score method performed the uncertainty analysis without continuity correction. In this method, the prediction error (ei), the mean prediction error (Mean), and the standard deviation of the error are (Se). If the mean error value in predicting the target variable is positive, the model's performance is Over Estimated (OS). Also, if the average value of the model error is negative, the model's performance is Under Estimated (US). Moreover, the Uncertainty Analysis results with a significance of 5% were obtained. Also, five approaches measured the performance of the models. The proposed approaches were: 1) Evaluation of prediction by accuracy chart, 2) Performance evaluation by mathematical indices, 3) Performance evaluation by Uncertainty Analysis by Wilson Score method without continuity correction, 4) Accuracy evaluation by error distribution charts, and 5) Performance evaluation by discrepancy rate (DR) charts. Finally, all the results are given at the end of each section, respectively. Findings The simulation was performed using artificial intelligence and regression models. The simulation results showed higher accuracy of artificial intelligence models. The results showed that different models were successful in predicting water parameters. Approach 1- According to the prediction accuracy charts, 16 charts were drawn, and the most accurate models are depicted in Figures 4 to 7. After modeling, the results showed that the most accurate models in simulating groundwater parameters were the SAELM model in GWL simulation. According to the results, the SAELM model in GWL and EC simulation, LSSVM in TDS simulation, and MLR in Salinity simulation were superior models.Approach 2- According to the performance measurement indices, the results showed that the SAELM model was the best in simulating parameters (EC) and (GWL). Then the LSSVM model was also the most accurate in modeling the (TDS) parameter. MLR was the best model in (Salinity) parameter simulation. Approach 3- Uncertainty analysis was performed based on the Wilson score method. The performance of the models in the simulation showed that the SAELM model's performance was underestimated, and other superior models in the simulation had overestimated performance. Approach 4- The best accuracy was assigned to SAELM and MLR models based on the error distribution diagrams. Approach 5- Based on the DR diagrams, SAELM and MLR models were the most accurate models in the simulation. Conclusion In all five performance approaches, the top models were introduced as follows:According to the performance measurement indices, each model successfully estimated one of the quantitative and qualitative parameters. Finally, study results showed that the SAELM was the best model for simulating EC and GWL parameters. It had the least computational error. Then LSSVM was also the most accurate model in estimating and modeling the TDS parameter. Also, MLR was the best model in Salinity parameter simulation. The uncertainty analysis results showed that the performance of the SAELM was underestimated, and other superior models were overestimated. The best accuracy was assigned to SAELM and MLR models based on the DR diagrams. SAELM and MLR models were the most accurate models based on the error distribution charts.Finally, it should be noted that based on the simulation results, the following predictions are possible: - The graphs gradually showed more insignificant changes in estimating GWL changes by the SAELM model. However, these changes will continue their downward procedure. - Based on the prediction diagrams, EC changes are almost like a time series and will continue similar to past behavior in the future changes. These changes will also occur in less time. - Based on the estimation of the TDS changes, the LSSVM charts behave like time series and seem the future behavior is similar to past time series. The changes occur in the same range. - Based on the estimation of Salinity changes, the MLR showed the changes of this parameter within a higher range. This tolerance occurred with higher values than before. [ABSTRACT FROM AUTHOR]

Abstract Qualitative analysis of water resources is one of the most widely used topics in water resources research today. Researchers use various analysis methods of water parameters to achieve the desired goals in this field. This research uses artificial intelligence (AI), learning machine (LM), data mining, and mathematical techniques to simulate water behavior and estimate its parametric changes. The proposed model used in this study was a Self-adaptive Extreme learning machine (SAELM) to estimate hydrogeological parameters of the Meghan wetland located in Markazi province in Iran. In addition, SAELM simulation results were compared to Least square support vector machine (LSSVM), Multiple linear regression (MLR), and Adaptive Neuro-fuzzy inference system (ANFIS) models. The simulated parameters were Electrical Conductivity (EC), Total Dissolved Solids (TDS), Groundwater Level (GWL), and salinity. This information was related to sampling for 175 months in the study area. Finally, after simulation operation, four models were introduced as superior models. Mentioned exceptional models were SAELM in GWL modeling, SAELM in modeling the EC, MLR in salinity simulation, and LSSVM in the simulation of TDS parameters. Moreover, by five approaches, the models' performance was evaluated. Suggested strategies were performance evaluation by statistical indicators, Wilson score method uncertainty analysis (WSMUA), response & correlation plots, discrepancy ratio charts, and distribution error diagrams. Based on statistical indicators, the SAELMGWL model was the most accurate model with RMSE, MAPE, and R 2 indices equal to 0.1496, 0.0043, and 0.9933, respectively. The ANFIS model had the worst results in simulation.

Qualitative analysis of water resources is one of the most widely used topics in water resources research today. Researchers use various analysis methods of water parameters to achieve the desired goals in this field. This research uses artificial intelligence (AI), learning machine (LM), data mining, and mathematical techniques to simulate water behavior and estimate its parametric changes. The proposed model used in this study was a Self-adaptive Extreme learning machine (SAELM) to estimate hydrogeological parameters of the Meghan wetland located in Markazi province in Iran. In addition, SAELM simulation results were compared to Least square support vector machine (LSSVM), Multiple linear regression (MLR), and Adaptive Neuro-fuzzy inference system (ANFIS) models. The simulated parameters were Electrical Conductivity (EC), Total Dissolved Solids (TDS), Groundwater Level (GWL), and salinity. This information was related to sampling for 175 months in the study area. Finally, after simulation operation, four models were introduced as superior models. Mentioned exceptional models were SAELM in GWL modeling, SAELM in modeling the EC, MLR in salinity simulation, and LSSVM in the simulation of TDS parameters. Moreover, by five approaches, the models' performance was evaluated. Suggested strategies were performance evaluation by statistical indicators, Wilson score method uncertainty analysis (WSMUA), response & correlation plots, discrepancy ratio charts, and distribution error diagrams. Based on statistical indicators, the SAELMGWL model was the most accurate model with RMSE, MAPE, and R2 indices equal to 0.1496, 0.0043, and 0.9933, respectively. The ANFIS model had the worst results in simulation. [ABSTRACT FROM AUTHOR]