India is an agricultural country and most of economy of India depends upon the agriculture. Rainfall plays an important role in agriculture so early prediction of rainfall plays an important role in the economy of India. Rainfall prediction has been the one of the most challenging issue around the world in last year. Widely used techniques for prediction are Regression analysis, clustering, and...

Rainfall prediction is very complex hydrologic process and is important as it holds the key to any countries’ economy. Proposed model presents a new approach for yearly rainfall prediction of 30 Indian subdivisions. Yearly rainfall data of the Indian subdivision is available from IITM, Pune. The combination of Fast Fourier Transform (FFT) and Feed Forward Neural Network (FFNN) is applied for ne...

Considering the fact that Iran is situated in an arid and semi-arid region, rainfall prediction for the management of water resources is very important and necessary. Researchers have proposed various prediction methods that have been utilized in such areas as water and meteorology, especially water resources management. The present study aimed at predicting rainfall amounts using Grey Predicti...

Rainfall prediction is one of the most important and challenging task in the modern world. In general, climate and rainfall are highly non-linear and complicated phenomena, which require advanced computer modeling and simulation for their accurate prediction. An Artificial Neural Network (ANN) can be used to predict the behavior of such nonlinear systems. ANN has been successfully used by most ...

precipitation data is of utmost importance to carry out many hydro-meteorological studies. observed warming over several decades has been linked to changes in the large-scale hydrological cycle such as: increasing atmospheric water vapour content, changing precipitation patterns, intensity and extremes, reduced snow cover and widespread melting of ice, and changes in soil moisture and runoff. p...

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is...

Prediction of monsoon rainfall for a mesoscale Indian catchment based on stochastical downscaling and objective circulation patterns E. Zehe, A. K. Singh, and A. Bardossy Institute of Geoecology, University of Potsdam, Germany Civil Engineering Department, Nirma University of Science and Technology (NU) Ahmedabad 382 481, India Institute of Hydraulic Engineering, University of Stuttgart, German...

Long lead rainfall prediction is important in the management and operation of water resources and many models have been developed for this purpose. Each of the developed models has its special strengths and weaknesses that must be considered in real time applications. In this paper, eld and General Circulation Models (GCM) data are used with the Statistical Downscaling Model (SDSM) and the Arti...

rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. in this study statistical method armax model, neural network, neuro-fuzzy (anfis subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate rainfall-runoff and prediction of streamflow. in each method optimum structure was deter...

Precise rainfall forecasting is a common challenge across the globe in meteorological predictions. As rainfall forecasting involves rather complex dynamic parameters, an increasing demand for novel approaches to improve the forecasting accuracy has heightened. Recently, Rough Set Theory (RST) has attracted a wide variety of scientific applications and is extensively adopted in decision support ...