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Groundwater contamination and vulnerability in urbanized areas are of major concern and need proper attention. Several models including the DRASTIC model are used to evaluate groundwater vulnerability. In the present study, a modified DRASTIC model named as DRASTICA was used, by including anthropogenic influence as a model parameter. The study included an innovative methodology to characterize the anthropogenic influence by using satellite observations of night-lights from human settlements as a proxy and land-use/landcover surrounding the urbanized area in Lucknow, the capital city of the most populous State of Uttar Pradesh in India. Geographical information system was used for spatial integration of different parameter maps. The groundwater vulnerability to pollution indicated that about 0.7 % area is covered under very high vulnerable zone, 24.5 % area under high vulnerable zone, 66.6 % area under moderately vulnerable zone and 8.2 % area under low vulnerable zone. The results were validated using nitrate concentration in ground water. It was shown that the proposed DRASTICA model performed better than conventional DRASTIC model in an urbanized environment. Sensitivity analysis indicated that anthropogenic impact and depth to water table largely influenced the groundwater vulnerability to pollution, thereby signifying that anthropogenic influence has to be addressed precisely in such studies. The modified-DRASTIC/DRASTICA model proposed in this study will help in better categorization of groundwater vulnerable zones to pollution where anthropogenic contamination is high, particularly in and around urban centers.

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their timefrequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.

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Please cite this article as: I. Singh, S. Kumar, Haar wavelet method for some nonlinear Volterra integral equations of the first kind, Journal of Computational and Applied Mathematics (2015), http://dx. Abstract:We present here a simple efficient Haar wavelet method for numerical solution of a class of nonlinear Volterra integral equations of the first kind. The present method is based on converting nonlinear Volterra integral equations of the first kind into linear Volterra integral equations of the second kind. Numerical examples are given to illustrate efficiency and accuracy of the present method. Comparison with numerical methods in the past has also been done. M SC: 65R20; 45G10; 45D05; 41A30

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