In this study, several data-driven techniques including system identification, time series, and adaptive neuro-fuzzy inference system (ANFIS) models were applied to predict groundwater level for different forecasting period. The results showed that ANFIS models out-perform both time series and...

Majuli was once the largest river islands and the cultural home of the Asomiya community. Today, repeated floods of the Brahmaputra have ensured that the community has lost home and hearth to erosion. This is the report on the first round of relief carried out in Majuli on 25th and 26th August...

We developed a real-time forecasting system, aiNet-GISPSRIL, for evaluating the spatiotemporal probability of occurrence of rainfall-triggered landslides. In this system, the aiNet (a kind of artificial neutral network based on a self-organizing system) and GIS are merged for integrating the...

For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful...

This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed through statistical models...

Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international...

A wavelet network model is developed to predict the inflow of Three Gorges dam in Yangtze River, China. The model makes use of the multi-resolution analysis of wavelet analysis and the nonlinear capability of artificial neural network. The short and long term input runoff of Three Gorges dam,...

Due to limited data sources, practical situations in most developing countries favor black-box models in real time operations. In a simple and robust approach, this study examines performances of stepwise multiple linear regression (SMLR) and artificial neural network (ANN) models, as tools for...

This work is concerned with forecasting water demand in the metropolitan area of São Paulo (MASP) through water consumption, meteorological and socio-environmental variables using an Artificial Neural Network (ANN) system. Possible socio-environmental and meteorological conditions affecting...

The conjunctive use of surface and subsurface water is one of the most effective ways to increase water supply reliability with minimal cost and environmental impact. This study presents a novel stepwise optimization model for optimizing the conjunctive use of surface and subsurface water...