India, with a population of 1.40 billion, is the most populous country in the world, necessitating increased food production. For making decisions about issues relating to food security, reliable crop yield estimation is essential. Knowing the expected yield of one's standing crops is crucial to farmers and can be a complicated task in and of itself. Modern artificial intelligence algorithms have shown to be highly useful tools for accurately predicting agricultural production. The primary focus of this study is on predicting the yield of five important crops grown in the Nashik region. Crop yield is significantly influenced by climatic variables such as rainfall, minimum and maximum temperatures, relative humidity, and evaporation. In this regard, we used the Decision tree (M5tree) method to predict the yield of five important crops farmed in the Nashik region of Maharashtra state in India: rice, jowar, maize, groundnut, and sugarcane. With acceptable accuracy, the developed models have functioned well. The association between climatic variables and the agricultural production of the crops under study was disclosed by the decision trees, and the rule accuracy validated this relationship.

Rainfall prognosis plays an important role in drought management and planning of drinking water and agricultural water resources. Also, Future policies can be tailored to optimize spending and maximum productivity. In this study, the effect of large-scale climatic signals on rainfall in Mazandaran province was investigated. The first, the effect of climatic signals on precipitation simultaneously and with delay was studied by statistical methods (Pearson correlation coefficient) and then, using the M5Tree model, monthly rainfall was compared with related indices. Generally, the correlation coefficient between signals and precipitation showed that correlation with delay was greater than the coincidence. The results of the correlation study between climate indices and monthly precipitation with a one-step delay showed that there was a significant correlation with, AMM, NINO1 + 2, NINO3, NINO4, TNA and WHWP indices and rainfall in the Babolsar station. Also, AMM, NINO1 + 2, NINO3, ONI, TNA and WHWP indices had a significant correlation with rainfall at the Gharakhil station. Based on the findings, correlation between climatic signals and rainfall in Noshahr station, was significantly different with AMM, NINO1 + 2, NINO3, NINO3. 4, TNA and WHWP indices. The correlation between Ramsar precipitation and climatic signals showed a significant difference with AMM, NINO1 + 2, NINO3, TNA and WHWP indices. The results of the M5Tree model indicated that, generally, in one-step delay, the best simulation performance will occur. Also, comparing the simulation results with Taylor's diagram showed that at all stations, the one-step delay values were closer to the observations. The advantage of delayed prediction is that the rainfall situation can be pre-determined and used to manage the water resources of the watershed.

Flow duration curve is one of the most important and applicable signals of hydrologic response of a basin. This curve was used for analyzing the frequency of low and flood flows of a river in many hydrologic uses. Also, the flow duration curve (FDC) was used to display the complete domain of river discharge from minimum up to maximum flood. Therefore, accurate derivation of this curves with the least error is necessary. In this study, applicability of M5 Tree Model in derivation of flow duration curve in Khazangah station located on Aras River, East Azerbaijan province was investigated and compared with the results of Artificial Neural Network (ANN) and Support Vector Machine (SVM) models. The results of M5Tree Model showed competition of 80 percent of data for training and the remaining for the testing has the best performance in presenting the flow duration curve with values of R2=0. 992, RMSE=5. 47 m3/s and MAE=4. 38 m3/s. The results of different structures of Neural Network showed the best model (2 neurons for hidden layer) was obtained with values of R2=0. 997, RMSE=3. 91 m3/s and MAE=3. 30 m3/s. Also the performance of RBF kernel of Support Vector Machine Showed this model has the best ability in simulation of flow duration curve, so that this model has lowest error values of RMSE=2. 98 m3/s, MAE=2. 66 m3/s and highest value of R2=0. 998. Comparison the results between the intelligence models showed that each three models have proper performance in determining the discharge values of flow duration curve. From the practical view, M5Tree Model has more applicability in derivation of flow duration curve because of the simplicity of the proposed equations and calculations.

Introduction and Objective: The correct estimation of flood flow in rivers is an important issue and plays a significant role in the optimal use of water resources, operation of dam reservoirs, and the design and planning of water projects. Material and Methods: In this research, a simple and conceptual method based on Manning's formula in real flow conditions is used to estimate the flood flow discharge. In this method, firstly, for the combined effect of friction slope and Manning's roughness coefficient, the alpha parameter (α) was defined and calculated for 12 hydrometric stations located in three main rivers of Golestan province (including Gorganrood, Atrak, and Qarasoo). Results: The results showed that the value of this parameter decreases continuously with the increase of the flow depth and finally asymptotically reaches a constant value. This behavior shows that the value of α is nearly constant for the upper flow depths which indicate the occurrence of floods, and hence using this constant value and the Manning formula, the river flood discharge can be estimated. In the next step, we tried to provide a regression model between the Alpha parameter and the flow depth. The regression modeling results showed that for most of the hydrometric stations, the coefficients of determination (R2) of the presented equations are smaller than 0.3 which demonstrates its low efficiency. For this reason, machine learning models were used and the parameter was modeled by the Artificial Neural Networks (ANN), Decision Tree (M5tree), and Support Vector Regression (SVR) models. Conclusion: The modeling results showed that the decision tree model with a mean absolute error of 0.35, determination coefficient of 0.88, and root mean square error of 0.86 has the best accuracy in the test phase. After determining the parameter α, the amount of flood discharge was predicted. The best performance among the models was the decision tree in predicting the flow rate in rivers. After comparing the observed values, the decision tree model has an average absolute error of 1.32, a determination coefficient of 0.89, and an average square root error of 63. 3. It has the best accuracy in the test phase.

Background and Objectives: Evaporation is one of the main components of hydrological cycle and one of the effective climatic variables in arid areas such as Iran. Accurate estimate of evaporation rate plays an important role in sustainable development and optimal management of water resources. Evaporation is one of the essential processes, because it depends on meteorological variables such as solar radiation, air temperature, wind speed, relative humidity and atmospheric pressure, which are related to the topography and the climate of the region. Class A pan-evaporation is one of the standard and direct tools for measuring evaporation, which is used all over the world due to its ease of application in determining evaporation. However, in most stations accurate evaporation recording is not practical due to instrument limitations and maintenance problems. On the other hand, the temporal and spatial distribution of evaporation stations compared to meteorological stations is limited, so according to the problems mentioned, the use of meteorological variables in estimating the rate of evaporation from the pan will be useful. In different regions, the impact of different climatic factors on changes evaporation from the pan has not be fully understood, so the relatively accurate estimation and prediction of this phenomenon is an effective step in the relevant fields. In recent years, for estimating the amount of evaporation from the pan, a variety of intelligent systems and software calculations such as data mining methods have been developed. Methodology: In this study, meteorological data of Tabriz station in the period of 2003 to 2018 have been used to estimate the evaporation values from the class A pan. For this purpose, a simple correlation between meteorological variables and evaporation from class A pan was created and based on the result of this correlation, in the studied station the minimum temperature and relative humidity were inversely and the maximum and average temperature were directly affected by evaporation. Thus, ten combined scenarios were defined and modeling was performed using Support vector regression (SVR), Gaussian process regression (GPR), M5tree, Random forest (RF) and Linear regression (LR) methods. It should be noted that in this study, 70% of the data were selected for training and 30% for testing. Finally, the performance of each method in estimating evaporation values was evaluated using root mean squared error (RMSE), mean absolute error (MAE), Nash- Sutcliffe coefficient (NS) and Akaike information criterion (AIC).Findings: The results showed that GPR10 method with RMSE = 1.90 mm/day, MAE = 1.48, NS = 0.81 and SVR10 method with RMSE = 1.92 mm/day, MAE = 1.51, NS = 0.8 had reasonable performance in estimating the values of daily evaporation from class A pan. The GPR method showed its higher capability to estimate daily evaporation values in all definition scenarios with the least error and the most accuracy. The SVR model with appropriate results was in the second place. The results of statistical parameters for random forest model were even weaker than the results of linear regression. In general, scenario number 10 with all meteorological variables and scenario number 1 with only the input minimum temperature variable had the best and weakest results among all defined scenarios, respectively. Scenarios 6 to 10 have more accuracy and less error and modeling structures with the least number of variables has the least accuracy. Also, wind speed and solar radiation variables were introduced as the most effective factors in estimating the evaporation rate from class A pan.Conclusion: Evaporation is one of the important processes that cause the losses of half of precipitation in arid and semi- arid regions. Accordingly, knowledge of the amount of evaporation and its modeling as one of the most important hydrological variables in agricultural research and factors related to water and soil of great importance. So, accurate estimation of this phenomenon is essential. In this study, meteorological data from Tabriz station were utilized to assessment capability of machine learning methods. Evaporation values were estimated using five data mining methods including SVR, GPR, M5, RF and LR. Conclusively, the results of evaluation criteria indicated that GPR and SVR models using all variable of meteorological data performed more accurate than others. Finally, both of them are recommended to estimate the amount of evaporation from class A pan.