The present study developed an artificial neural network (ANN) model to overcome the difficulties in training the ANN models with continuous data consisting of rainy and non-rainy days. Among the six models analyzed the ANN model which used generalized feedforward type network and a hyperbolic tangent function and a combination of 5 meteorological parameters (relative humidity, air pressure, we...

In the recent years the important scientific challenge faced by researchers worldwide is to have a better understanding about climate change at a regional scale. However, for all the regions the changes are unequal and have localized intensity. Therefore it should be quantified at a local scale. Precipitation is one of the important climatic variables responsible for climate change and required...

The practical experience with sensitivity analysis suggests that no single-objective function is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. In order to successfully measure parameter sensitivity of a numerical model, multiple criteria should be considered. Sensitivity analysis of a rainfall-runoff model is performed using t...

We update the Global Inventory Modeling and Mapping Studies (GIMMS) analysis of Sahelian vegetation dynamics and trends using the normalized difference vegetation index (NDVI; version 3g) 1981 to 2012 data set. We compare the annual NDIV3g and July to October growing season averages with the three rainfall data sets: the Africa Rainfall Climatology from 1983 to 2012, the Variability Analyses of...

Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. [1] We develop a methodology for the frequency of extreme rainfall intensities caused by tropical cyclones (TCs) in...