The prediction of groundwater levels in a well has immense importance in the management of groundwater resources, especially in arid regions. This paper investigates the abilities of neurofuzzy (NF) and artificial neural network (ANN) techniques to predict the groundwater levels. Two different NF and ANN models comprise various combinations of monthly variablities, that is, air temperature, rainfall and groundwater levels in neighboring wells. The result suggests that the NF and ANN techniques are a good choice for the prediction of groundwater levels in individual wells.Also based on comparisons, it is found that the NF computing techniques have better performance than the ANN models in this case.
River water quality is a significant concern in many countries, considering agricultural and drinking consumptions. Therefore, prediction of salinity index, as the main water quality condition is a necessary tool for water resources planning and management. This paper describes the application of artificial neural networks (ANNs) models for computing the total dissolved solids (TDS) level in Jajrood River (Iran). Two ANN networks, multi-layer perceptron (MLP) and radial basis function (RBF), were identified, validated and tested for the computation of TDS concentrations. Both networks employed five input water quality variables measured in river water over a period of 40 years. The performance of the ANN models was checked through the coefficient of determination (R 2 ) and root mean square error (RMSE). Jajrood River is one of the most important rivers which is located adjacent to Tehran city and supplies drinking water for people who live in this mega-city and recreational uses. Tehran is the most populous city and largest industrial pole in Iran, which caused the river, to be exposed to various pollutants. Matlab 2007 was selected for modeling goals in this research. Results show that MLP and RBF modeling as two methods of ANN are able to simulate water quality variables of Jajrood River with more than 90% accuracy. After modeling in MLP and RBF formatting and comparing simulation results (output) show that, the RBF result (R 2 of validation is 0.9362) are more closely to reality than the MLP (R 2 of validation is 0.8968). In other words, because of large number of input data, the RBF modeling performance has a better prediction than MLP modeling.
In order to evaluate risk elements and their spatial distribution in agricultural fields south of Tehran, statistics, geostatistics and geographic information system (GIS) were used. The content of Hg, As, Pb, Mo, and Be were determined in 106 samples. The results showed that primary inputs of As and Hg were due to anthropogenic sources, while Pb, Mo and Be were associated with pedogenic and anthropogenic factors. Ordinary kriging was carried out to map the spatial patterns of risk elements and disjunctive kriging was used to quantify the probability of risk elements concentrations higher than their guide value. The results show that As, Hg, Mo and Be exhibit pollution risk in the study area. The high pollution sources evaluated were related with usage of urban and industrial wastewater for agricultural practice. The results of this study are helpful for risk assessment of environmental pollution for decision making for vegetable production and ecosystem improving.
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