Artificial neural network modeling of the water quality index using land use areas as predictors

This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were employed in the modeling process. The most accurate WQI predictions were obtained with the network architecture 7-23-1; the back propagation training algorithm; and a learning rate of 0.02. The WQI forecasts of this model had significant (p rubber > forest > logging > urban areas > agriculture > oil palm. These findings show that the ANNs are highly reliable means of relating water quality to land use, thus integrating land use development with river water quality management

Ground water quality in Wadi Shati (Libya): Physicochemical analysis and environmental implications

This study aimed at evaluating water quality of groundwater wells (GWWs) in Wadi Shati, Libya, and assessing its suitability for drinking. Water samples were collected from 17 GWWs and subjected to laboratory testing for 24 physical and chemical water quality parameters (WQPs). Analysis uncovered that the recorded values of 11 WQPs were consistent with the Libyan drinking water quality standard (DWQS). These parameters were pH, temperature (T), acidity, alkalinity, electrical conductivity (EC), sodium, potassium, calcium, magnesium, zinc, and cadmium. However, values of colour and turbidity exceeded the maximum levels set by the Libyan DWQS at five out of the 17 study wells. Likewise, concentrations of chloride (Cl-), sulphate (SO4 2-), and ammonia (NH3) violated the local DWQS in three locations, each. Additionally, concentrations of phosphate (PO43-), iron, manganese, chromium, and nickel exceeded their maximum allowable concentrations according to the Libyan DWQS. The levels of these five parameters are alarming. Overall, the 17 studied GWWs suffer from varying levels of pollution that, mostly, arise from domestic and agricultural sources, e.g., septic tank seepage and agricultural drainage of agro-chemicals like fertilisers and pesticides. The results of this study emphasise that routine monitoring of groundwater resources plays a vital role in their sustainable management and stresses that water quality data are critical for characterisation of pollution, if any, and for protection of human health and ecosystem safety. Our results serve as guideline for sustainable management of water quality in the Wadi Shati District