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Precipitation forecasting plays a key role in natural resource management, agriculture management, and water requirement provision. Hence, dendroclimatology methods and artificial neural network (ANN) are used to estimate precipitation values in the past times. Moreover, a geographic information system (GIS) can be applied as a tool to demonstrate the spatial variation of precipitation. In this study, earlywood vessel features and tree-rings of Siberian elm species were used to simulate precipitation. The vessel features, the tree-ring extent, and the secondary data of climatologic station from the different sites were studied. At first, cross-dating and standardization of the tree-rings and vessels were performed. Then, time series analysis was done. In the next step, the relations between vessel chronologies and tree-rings with the precipitation were defined. The input parameters were selected tree-ring width and vessel features for the modeling, whereas precipitation of the growing season was selected as the output. The model or network was trained and verified by taking a case study of Fomanat plain (northern part of Iran). The results showed that the combinatory usage of vessel features and tree-rings extent in precipitation simulating can increase simulation capability. Further, different isohyets were generated using the simulated precipitation values, an interpolation technique in a GIS system. The results showed that the highest precipitation and the lowest precipitation have occurred in 1926 and 1986 during the last century. [ABSTRACT FROM AUTHOR]

Increased nitrate concentration is one of the main groundwater quality problems today that needs to be measured and monitored. Water quality testing and monitoring are time consuming and costly. Therefore, new modeling methods such as machine learning algorithms can be used as an efficient solution for predicting nitrate concentration. In this study, three machine learning methods including deep neural network (DNN), extreme gradient boosting (EGB), and multiple linear regression (MLR) were used to predict nitrate contamination in groundwater in the north of Iran (Mazandaran plain) and finally the best method was selected for mapping. The mean nitrate concentration in 250 piezometric wells was considered as output variable and the factors affecting groundwater quality (groundwater depth, transmissivity of aquifers, precipitation, evaporation, distance from water resources and Caspian Sea, distance from industries and residential centers, population density, topography, and exploitation from groundwater) as input variables in an alluvial aquifer. The same training and testing data were used in the modeling process of the three machine learning methods. The results of the training and testing stages showed that the EGB method has the highest performance in predicting nitrate concentration due to the lowest error values and highest correlation between the measured and predicted values of nitrate concentration (training R-sqr = 0.98, Nash–Sutcliffe efficiency (NSE) = 0.98, and test R-sqr = 0.86, NSE = 0.84). Further, the results indicate that the factors distance from industries, population density, groundwater depth, and evaporation rates are the most important factors affecting nitrate concentration in groundwater. Finally, the tested EGB model and a geographic information system (GIS) tool were used to prepare a map of groundwater nitrate pollution in the study area. Evaluating the performance of the resulting map by comparing the predicted and measured values indicated a good accuracy (R-sqr = 0.8). • Machine learning was used for modeling nitrate concentration in groundwater. • The extreme gradient boosting model yielded more favorable results than the deep neural network and multiple linear regression methods. • The verification of the results showed the performance of the adopted methodology in nitrate concentration mapping. [ABSTRACT FROM AUTHOR]