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Artificial neural networks (ANNs) have become one of the most promising tools for rainfall simulation since a few years ago. However, most of the researchers have focused on rainfall intensity records as well as on watersheds, which generally are utilized as input records of other hydro-meteorological variables. The present study was conducted in Kechik station, Golestan Province (northern Iran). The normal multi-layer perceptron form of ANN (MLP–ANN) was selected as the baseline ANN model. The efficiency of GDX, CG and L–M training algorithms were compared to improve computed performances. The inputs of ANN included temperature, evaporation, air pressure, humidity and wind velocity in a 10 minute increment The results revealed that the L–M algorithm was more efficient than the CG and GDX algorithm, so it was used for training six ANN models for rainfall intensity forecasting. The results showed that all of the parameters were proper inputs for simulating rainfall, but temperature, evaporation and moisture were the most important factors in rainfall occurrence.

Summary Groundwater is the most important water resource in semi-arid and arid regions such as Iran. It is necessary to study groundwater level fluctuations to manage disasters (such as droughts) and water resources. Dendrochronology, which uses tree-rings to reconstruct past events such as hydrologic and climatologic events, can be used to evaluate groundwater level fluctuations. In this study, groundwater level fluctuations are simulated using dendrochronology (tree-rings) and an artificial neural network (ANN) for the period from 1912 to 2013. The present study was undertaken using the Quercus Castaneifolia species, which is present in an alluvial aquifer of the Caspian southern coasts, Iran. A multilayer percepetron (MLP) network was adopted for the ANN. Tree-ring diameter and precipitation were the input parameters for the study, and groundwater levels were the outputs. After the training process, the model was validated. The validated network and tree-rings were used to simulate groundwater level fluctuations during the past century. The results showed that an integration of dendrochronology and an ANN renders a high degree of accuracy and efficiency in the simulation of groundwater levels. The simulated groundwater levels by dendrochronology can be used for drought evaluation, drought period prediction and water resources management. [ABSTRACT FROM AUTHOR]