Aguas subterráneas se bombean en gran cantidad desde la parte costera en las cuencas de los ríos Arani, en Tamil Nadu, India, para el riego y el aprovisionamiento de agua a la ciudad de Chennai. El objetivo de este estudio es delinear las zonas potenciales de aguas subterráneas en esta área a través de sistemas de Teledeteción (RS) y de Información Geográfica (GIS). Se hizo un análisis sobrepuesto compensado para demarcar las zonas con posibilidad de tener aguas subterráneas. Se prepararon mapas de uso de la tierra con varios elementos temáticos como geología, geomorfología, terreno, densidad de lineamiento, densi- dad de drenaje y precipitación. El mapa geológico fue preparado a partir del Mapa Distrital de Recursos, del Servicio Geológico de la India (GSI). Fueron utilizadas imágenes satelitales Sensing System Linear Imaging Self-scanning Sensor III (IRS-1D LISS III) para definir la geomorfología, terreno, densidad de lineamientos, densidad de drenaje y mapas de uso del terreno. El mapa final de zonas potenciales de aguas subterráneas se preparó con la superposición apropiada de diferentes mapas temáticos sobre los mapas de aguas subterráneas potenciales. Para la validación apropiada, el mapa derivado se solapó con los de niveles de aguas subterráneas y de zonas de pozos. El mapa preparado ayudará en el desarrollo sistemático y apropiado de las fuentes hídricas subterráneas en esta área que permita abastecer la creciente demanda de agua de la ciudad de Chennai.

Groundwater is being pumped extensively from the coastal part of the Arani and Koratalai River Basin, Tamil Nadu, India for irrigation and water supply to the city of Chennai. The objective of this study is to delineate the groundwater potential zones of this area using Remote Sensing (RS) and Geographic Information System (GIS) techniques. Weighted overlay analysis was used to demarcate the ground- water potential zones. Various thematic layers such as geology, geomorphology, soil, lineament density, drainage density, rainfall and landuse maps were prepared. The geological map was prepared using a Geological Survey of India (GSI) district resource map. Indian Remote Sensing System Linear Imaging Self-scanning Sensor III (IRS-1D LISS III) satellite imagery was used to prepare the geomorphology, soil, lineament density, drainage density, and landuse maps. The final groundwater potential map was prepared by assigning appropriate weightage to different thematic maps and adding them to the final groundwater potential map. The derived groundwater potential map was overlaid with the groundwater level and location of well fields for validation. The map prepared will help in systematic and proper development of groundwater resources in this area to meet the growing water requirements of the city of Chennai.

This paper presents Artificial Neural Network (ANN) implementation for the Radio Frequency (RF) and Mechanical modeling of lateral RF Micro Electro Mechanical System (MEMS) series micro machined Single pole double through (SPDT) switch. We propose an efficient approach based on ANN for analyzing the losses in ON and OFF state of lateral RF MEMS series switch by calculating the S-parameters. The double beam structure has been analyzed in terms of its return, isolation and insertion losses with the variation of its passive circuit component values. The effect of design parameters has been analyzed and the lateral switch was realized with low insertion loss, high return and isolation losses. ANN model were trained with five different training algorithms namely Levenberg-Marquart (LM), Bayesian Regularization (BR), Quasi - Newton (QN), Scaled Conjugate Gradient (SCG) and Conjugate Gradient of Fletcher - Powell (CGF) to obtain better performance and fast convergence. The results from the neural model trained by Levenberg-Marquardt back propagation algorithm are highly agreed with the theoretical results available in the literature. The neural networks shows the better results with the highest correlation coefficient which measures the strength and direction of linear relation between two variables (actual and predicted values) (0.9998) along with lowest root mean square error (MSE) of (0.0039).