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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]

The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study's results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low. [ABSTRACT FROM AUTHOR]