The paper highlights the growing concern of the impact of climate change and variability including rainfall anomaly, rising temperature in mountain areas and occurrence of heat waves in relation to human mortality pattern in India. The paper investigates the historical perspective of rainfall and malaria relationship, and cited current studies to show how climate change and variability resulted...

In this work a feed-forward NN based NAR model for forecasting time series is presented. The learning rule used to adjust the NN weights is based on the LevenbergMarquardt method. In function of the long or short term stochastic dependence of the time series, we propose an online heuristic law to set the training process and to modify the NN topology. The approach is tested over five time serie...

This paper reports results obtained using artificial neural networks (ANN) models for shortterm river flow forecasting under heavy rain storms, in the upper Serpis river basin (460 km), with the outlet in Beniarrés reservoir (29 hm ). The system is monitored by 6 raingauges, providing 5-min rainfall intensities, while reservoir inflows are derived from depth measurements in the reservoir every ...

This paper explores the use of flow length and travel time as a pre-processing step for incorporating spatial precipitation information into Artificial Neural Network (ANN) models used for river flow forecasting. Spatially distributed precipitation is commonly required when modelling large basins, and it is usually incorporated in distributed physically-based hydrological modelling approaches. ...

The paper presents a comparison of lumped runoff modelling approaches, aimed at the realtime forecasting of flood events, based on or integrating Artificial Neural Networks (ANNs). ANNs are used in two ways: (a) as black-box type runoff simulation models or (b) for the real-time improvement of the discharge forecasts issued by a conceptual-type rainfall-runoff model. As far as the coupling of A...

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theor...

While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangt...

Forecasting river discharge is a very important issue for the prediction and monitoring of ground effects related to severe precipitation events. The meteorological forecast systems are unable to predict precipitation on small spatial (few km) and temporal (hourly) scales. For these reasons the issuing of reliable flood forecasts is not feasible in those regions where the basin’s response to ra...

This paper presents a study of neural network model for prediction of Indian rainfall. The purpose of this paper is to evaluate the applicability of ANN. In this paper the performance of different networks have been evaluated and tested.The multilayered artificial neural network with learning by backpropagation algorithm is used .The paper implements weather prediction by building training and ...

The highway slope failures are triggered by the rainfall, namely, to create the disaster. However, forecasting the failure of highway slop is difficult because of nonlinear time dependency and seasonal effects, which affect the slope displacements. Starting from the artificial neural networks ANNs since the mid-1990s, an effective means is suggested to judge the stability of slope by forecastin...