Introduction Life on Earth is influenced by precipitation. Precipitation is one of the most significant factors that affect the hydrological cycle. Considering that precipitation is non-linear, complex, and can be changed according to spatial and temporal, estimating the amount of this important atmospheric factor in each month or year for each region and watershed is particularly important in managing and optimizing water resources. Various optimization models and algorithms have been proposed for modeling hydrological systems in recent decades. These algorithms have significantly reduced errors and increased accuracy. Still, since hydrological systems rely on random events, none of the methods can be completely and accurately selected as a superior model for modeling and estimating. The honey badger algorithm is an innovative algorithm that requires a few iterations to achieve an optimal solution, and this increases the speed of reaching the desired results. In current study investigates the performance of three models, including multiple linear regression (MLR), artificial neural network (ANN), and hybrid artificial neural network with honey badger optimization algorithm (HBA-ANN) for modeling the temporal and spatial precipitation in East Azarbaijan province. The best-developed model was selected by evaluation criteria such as R, RMSE, NRMSE, MBE, and NSE, the best model is selected. Materials and Methods The MLR model is one of the methods to analyze and investigate several variables. In this method, the model has one dependent variable and several independent variables, so that a linear equation is generated between the independent variables called X1, X2, ..., Xn and the dependent variable Y. ANN is a black box model of neural networks in the human brain. One of the most used methods is the BP method, which includes two stages. In the first stage, which is entitled feed-forward, the error value is calculated, after comparing output and objective values. In the second stage, which is labeled the back-propagation, the error value calculated in the previous step is corrected. The mentioned two stages continue until the output of the model approaches the desired output. The HBA is a new algorithm that simulates the honey-seeking behavior of a creature called the honey badger. The HBA includes two stages. In the first phase, the locations of this creature are calculated, and in the second phase, the exact distance between the HBA and the prey (dj) is calculated based on the honey intensity (S) and the honey smell intensity (Ij), as well as its new and optimal location for the prey Xnew. In the HBA-ANN model, the HBA algorithm is used to determine the most optimal output value in the ANN and increase performance in modeling. Therefore, the developed hybrid model can have the characteristics of both ANN and HBA methods. Results and Discussion In this study, in the first stage, the temporal modeling, and in the second stage, the spatial modeling of the monthly precipitation of 18 stations in East Azarbaijan province during the period of 1996-2022 using MLP, ANN, and HBA-ANN models has been paid. For temporal modeling of precipitation, one and two-month precipitation delay steps of the stations were used as input parameters. The first 70% of the dataset was selected for the training phase and the last 30% of the dataset was selected for the testing phase. Based on the results obtained from evaluation criteria and graphic diagrams, it can be concluded that the HBA-ANN model indicated significant accuracy compared to other models in the temporal modeling of precipitation. Also, by comparing the results of the stations in the HBA-ANN model, the Heris station with R =0.94, RMSE=2.25, NSE=0.79, NRMSE=0.04, and MBE=1.06 in the testing stage performed better compared with other stations. For spatial modeling of precipitation, the geographic coordinates of the stations, which include longitude, latitude, and altitude, are used as input parameters, and average monthly precipitation is used as the output parameter. From eighteen stations, 70% of the stations were selected for the training phase and 30% of the stations were selected for the testing phase. Based on the results obtained from R=0.95, RMSE=1.03, NSE =0.92, NRMSE = 0.03, and MBE = -0.81 and graphical diagrams, it can be concluded that the HBA-ANN model revealed significant accuracy compared to other models in spatial modeling of precipitation. Conclusion Precipitation is one of the most important factors that significantly change the hydrological cycle. Therefore, modeling and estimating this parameter is vital. In this study, the performance of multiple linear regression (MLR), artificial neural network (ANN), and hybrid ANN using honey badger algorithm (HBA-ANN) models were used for the spatial and temporal modeling of precipitation in East Azarbaijan province. For spatial modeling, the time delay steps of one and two months of station precipitation were selected as input parameters. Also, for temporal modeling, the longitude, latitude, and altitude parameters were used. The mentioned models were evaluated by R, RMSE, NSE, NRMSE, and MBE assessment criteria. According to the results of temporal modeling, the HBA-ANN model for all stations, especially Heris station with R equal to 0.94, RMSE equal to 2.25, NSE equal to 0.79, NRMSE equal to 0.04, and MBEequal to 1.06 is selected as the superior model. Also, based on the results obtained from spatial modeling, the HBA-ANN model with R equal to 0.95, RMSE equal to 1.03, NSE equal to 0.92, NRMSE equal to 0.03, and MBEequal to -0.81 was selected as the best model. The MLR and ANN models, respectively, presented a relatively poor performance compared to the developed hybrid model.

One of the important issues in the analysis of soils is to evaluate their features. In estimation of the hardly available properties, it seems the using of Data mining is appropriate. Therefore, the modelling of some soil quality indicators, using some of the early features of soil which have been proved by some researchers, have been considered. For this purpose, 140 disturbed and 140 undisturbed soil samples were collected from Jiroft, southern Kerman, Iran. Some physical and chemical properties of soil, for example, sand, silt and clay percentage, organic matter (OM), calcium carbonate (CaCO3), electrical conductivity at saturation (ECe), porosity (F), and bulk density (BD) were measured using standard methods. Some soil physical property indicators, including plant available water (PAW), relative field capacity (RFC), air capacity (AC) and saturated hydraulic conductivity (Ks) were also calculated. Using the hybrid algorithm of principle component analysis-artificial neural network (PCA-ANN), the calculated indicators were predicted by the easily available properties. The results showed that PCA-ANN had an acceptable accuracy in the modelling of soil physical quality. The coefficient of determination (R2) of training and testing data for PAW, RFC and AC were 0. 82 and 0. 81, 0. 90 and 0. 79, 0. 99 and 0. 99, respectively. The optimization of Ks did not have the desired results. In other words, the R2 values of the training and testing data for this indicator were equal to 0. 25 and 0. 13, respectively.

Doubtlessly the first step in a river management is precipitation prediction of the watershed area. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently Artificial Neural Network (ANN) is extensively used as a non-linear inter-extrapolator by hydrologists. In the present study, Wavelet Analysis combined with artificial neural network and compared with Artificial Neural Network to predict the precipitation of Varayeneh station in the city of Nahavand. For this purpose, the original time series using wavelet theory decomposed to multi sub-signals. After this these sub-signals are used as input data to Artificial Neural Network to predict monthly Precipitation. The results showed that according to correlation coefficient of 0.92 and mean square error of 0.002 for the hybrid model of Wavelet- Artificial Neural Networks, the performance of this model is better than Artificial Neural Network with correlation coefficient of 0.75 and mean square error of 0.003 and can be used for short and long term precipitation prediction.

Introduction Due to global warming, accurately estimating evaporation has become a key challenge in water resource management, and due to the important role it plays in the withdrawal of water from human reach, it has always attracted the attention of researchers. Therefore, modeling and awareness of the value of evaporation as one of the hydrological variables is of great importance in agricultural research and soil and water conservation. Gorgan was chosen for the study due to its proximity to the Caspian Sea with a humid climate and a higher rate of evaporation than other cities. On the other hand, Shiraz has a hot and dry climate, is located in central Iran far from water resources such as the sea, and has a lower evaporation rate. Kish also has a warm and humid climate due to its proximity to the sea, with a lower evaporation rate than Shiraz but higher than Gorgan. Several meteorological variables affect the process of evaporation and transpiration, and due to the complexity of the evaporation parameter, a method with high accuracy should be used to determine them. Recently, artificial neural network methods have become very popular among researchers due to their common use and the ease with which they can solve complex problems. Therefore, many intelligent algorithms have been suggested to find the best solution for complex engineering problems, as they can find optimal answers faster and more accurately.   Materials and Methods Artificial neural networks are designed based on inspiration from the memory and learning mechanisms in the human brain. To train artificial neural networks, a set of valid input and output data is used based on the type of problem. The accuracy of the network output depends on the amount of training data and how the inputs and their features are processed. To design different scenarios for adjusting input data, the correlation values of the data with evaporation were used. In this study, three synoptic stations with different climates, including Gorgan, Shiraz, and Kish, were chosen. Three stations with different climates were used to better evaluate and repeat the steps of the method so that the efficiency of the method could be more accurately assessed. Considering the importance of the value of evaporation in nature, evaporation modeling with the ANN and its combination with the COOT algorithm, which mimics the natural life of a COOT bird, was performed using five meteorological parameters, including the minimum air temperature, maximum air temperature, wind speed, average relative humidity, and sunshine hours on a monthly between 2000 and 2022. The dataset was divided into two phases: training (70 % of the dataset) and testing (30% of the dataset). To evaluate the performance of developed models, statistical indices of these models such as correlation coefficient (R), root-mean-square error (RMSE), Nash-Sutcliffe coefficient (NS), and their graphical representations were compared with each other.   Results and Discussion As mentioned, four models of ANN-COOT with varying input parameters were developed and compared to four conventional ANN models. Statistical performances were calculated, and comparison plots were made in the training and testing phases to find the most adequate model for the prediction of evaporation. Comparing of obtained results from statistical indices for the testing phase revealed that the COOT-ANN4 model had the best performance for Gorgan with the R, RMSE, and NS equal to 0.99, 8.19, and 0.99 respectively. Shiraz also obtained values of the R, RMSE, and NS equal to 0.99, 18.43, and 0.98 respectively. Similarly, for Kish, the values of the R, RMSE, and NS equal to 0.97, 19.36, and 0.93 respectively, have better performance than the other models. Compared with the results of different input combinations, the hybrid ANN-COOT model (ANN-COOT4) at three stations was found superior with input combinations of Tmin, Tmax, SSH, RH, and WS. Additionally, to evaluate the accuracy of developed models, Scatter plots, Violin plots, Relative error percent plots (RE %), Taylor diagrams, and Histograms were drawn. By comparing the graphical representations, it can be determined that the hybrid COOT with ANN4, namely the COOT-ANN4 model, has improved the artificial neural network at Gorgan, Shiraz, and Kish stations.   Conclusion The algorithm of COOT is an optimization algorithm that is generally used to solve optimization problems. As observed from the overall performance of the results of the hybrid model in predicting evaporation, the objective function was minimized. The results indicated that scenario four of the COOT-ANN4 hybrid model with input parameters of minimum temperature, maximum temperature, sunshine hours, relative humidity, and wind speed has better accuracy and performance at all three stations. In general, the findings of this study revealed that the COOT algorithm can improve the artificial neural network (ANN) structure in any climate and provide a hybrid model with higher accuracy and less error for modeling the evaporation parameter. Considering that the COOT algorithm is powerful and efficient, it is better to use it in various fields to improve the performance and accuracy of models. The testing results revealed that the lowest Root Mean Square Error RMSE (18.43, 19.36 and 8.19) and highest coefficient of correlation R (0.99, 0.97, and 0.99), and the highest Nash–Sutcliffe Efficiency Coefficient (N-S) (0.98, 0.93 and 0.99) attained by the ANN-COOT4 hybrid model (relative to other ANN and ANN-COOT models) tested for three selected stations in Shiraz, Kish and Gorgan sites. Concerning the predictive efficiency, the developed ANN-COOT hybrid model, improved the modeling performance at extreme points, which outperforms the ANN model, indicating its capability in the prediction of monthly evaporation.

Background and ObjectivesPore 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.Methodology 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. FindingsBy 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.Conclusion: 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.

Time series prediction is a remarkable research interest, which is widely followed by scientists / researchers. Because many fields include analyzing processes over such time series, different kinds of approaches, methods, and techniques are often employed in order to achieve alternative prediction ways. It seems that Artificial Intelligence oriented solutions have strong potential on providing effective and accurate prediction approaches in even most complicated time series structures. In the sense of the explanations, this study aims to introduce an alternative, Artificial Intelligence based approach of Artificial Neural Networks, and Cognitive Development Optimization Algorithm, a recent intelligent optimization technique introduced by the authors. Here, it has been aimed to predict different kinds of time series, by using the introduced system / approach. In this way it has been possible to discuss about application potential of the hybrid system and report findings related to its success on prediction. The authors believe that the study has been a good chance to support the literature with an alternative solution approach and see potential of a newly developed, Artificial Intelligence oriented optimization algorithm on different applications.

Estimation of evapotranspiration is essential for planning, designing and managing irrigation and drainage schemes, as well as water resources management. In this research, artificial neural networks, neural network wavelet model, multivariate regression and Hargreaves' empirical method were used to estimate reference evapotranspiration in order to determine the best model in terms of efficiency with respect to the existing data. The daily data of two meteorological stations of Shahrekord and Farrokhshahr airport in the dry and cold zones of Shahrekord during the period 2013-2004 was used; these included the minimum and maximum temperature, the average nominal humidity, wind speed at 2 meters height and sunshine hours. %75 of the data were validated, and %25 of the data was used for testing the models. Designed network is a predictive neural network with an active sigmoid tangent function hidden in the layer. In the next step, different wavelets including Haar, db and Sym were applied on the data and the neural network-wavelet was designed. To evaluate the models, the method was used by the Penman-Montith Fao and for all four methods, RMSE, MAE and R statistical indices were calculated and ranked. The results showed that the wave-let-neural network with the db5 wavelet had a better performance than other wavelets, as well as the artificial neural network, multivariate regression and the Hargreaves method. The results of wavelet network modelling with the db5 wavelet in the Farrokhshahr station were calculated to be 0. 2668, 0. 2067 and 0. 998, respectively; at the airport station, these were equal to 0. 2138, 0. 14 and 0. 9989, respectively. The results, therefore, showed that the neural network-wavelet performance was more accurate than the other models studied in this study.

Background: Prediction of future climate change is based on output of global climate models (GCMs). However, because of coarse spatial resolution of GCMs (tens to hundreds of kilometers), there is a need to convert GCM outputs into local meteorological and hydrological variables using a downscaling approach. Downscaling technique is a method of converting the coarse spatial resolution of GCM outputs at the regional or local scale. This study proposed a novel hybrid downscaling method based on artificial neural network (ANN) and particle swarm optimization (PSO) algorithm. Materials and Methods: Downscaling technique is implemented to assess the effect of climate change on a basin. The current study aims to explore a hybrid model to downscale monthly precipitation in the Minab basin, Iran. The model was proposed to downscale large scale climatic variables, based on a feed-forward ANN optimized by PSO. This optimization algorithm was employed to decide the initial weights of the neural network. The National Center for Environmental Prediction and National Centre for Atmospheric Research reanalysis datasets were utilized to select the potential predictors. The performance of the artificial neural network-particle swarm optimization model was compared with artificial neural network model which is trained by Levenberg– Marquardt (LM) algorithm. The reliability of the models were evaluated by using root mean square error and coefficient of determination (R2). Results: The results showed the robustness and reliability of the ANN-PSO model for predicting the precipitation which it performed better than the ANN-LM. It was concluded that ANN-PSO is a better technique for statistically downscaling GCM outputs to monthly precipitation than ANN-LM. Discussion and Conclusions: This method can be employed effectively to downscale large-scale climatic variables to monthly precipitation at station scale.

Research on vehicle longitudinal control with a stop and go system is presently one of the most important topics in the field of intelligent transportation systems. The purpose of stop and go systems is to assist drivers for repeatedly accelerate and stop their vehicles in traffic jams. This system can improve the driving comfort, safety and reduce the danger of collisions and fuel consumption. Although there have been many attempts to model stop and go maneuver via traffic models, but predicting the future vehicle's acceleration in steps ahead has not been studied much in this models. The main contribution of this paper is in designing integrated genetic algorithm-artificial neural network (GA-ANN) which is a soft computing method to simulate and predict the future acceleration of the stop and go maneuver for different steps ahead based on US federal highway administration’s NGSIM dataset in real traffic flow. The results of this study are compared with two methods, back propagation based artificial neural network model (BP-ANN) and standard time series forecasting approach called ARX model. The mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination or R-squared (R2) are utilized as three criteria for evaluating predictions accuracy. The results showed the effectiveness of the proposed approach for prediction of driving acceleration time series. The proposed model can be employed in intelligent transportation systems (ITS), collision prevention systems (CPS) and driver assistant systems (DAS) such as adaptive cruise control (ACC) and etc. The outcomes of this study can be used for the automotive industries who have been seeking accurate and inexpensive tools capable of predicting vehicle speeds up to a given point ahead of time, known as prediction horizon, which can be used for designing efficient predictive controllers based on human behaviors.