More accurate forecasting of monthly rainfall is significantly important in drought forecasting in agriculture, irrigation schedule, water resources management, and crop pattern design. In this paper, ability of time series models in forecasting the rainfall according to the climate conditions is estimated. For this purpose, rainfall data of four different climates in Iran was selected. Using t...

This paper presents an Artificial Intelligence approach for regional rainfall forecasting for Orissa state, India on monthly and seasonal time scales. The possible relation between regional rainfall over Orissa and the large scale climate indices like El-Niño Southern Oscillation (ENSO), EQUitorial INdian Ocean Oscillation (EQUINOO) and a local climate index of Ocean-Land Temperature Contrast (...

This paper reports the study of the effect of the length of the recorded data used for monthly rainfall forecasting. Monthly rainfall data for three periods of 5, 10, and 49 years were collected from Kermanshah, Mashhad, Ahvaz, and Babolsar stations and used for calibration time series models. Then, the accuracy of the forecasting models was investigated by the following year’s data. The follow...

A framework for short-term probabilistic forecasting of watershed flood stage conditions using a distributed rainfall-runoff model is proposed. A stochastic rainfall pattern simulation model capable of generating input for a distributed rainfall-runoff model is developed. Generation of rainfall patterns over a 6-hour period is achieved using a translation vector rainfall forecasting process mod...

Rainfall forecasting is an important research topic in disaster prevention and reduction. The characteristic of rainfall involves a rather complex systematic dynamics under the influence of different meteorological factors, including linear and nonlinear pattern. Recently, many approaches to improve forecasting accuracy have been introduced. Artificial neural network (ANN), which performs a non...

Time-series analysis techniques for improving the real-time flood forecasts issued by a deterministic lumped rainfall-runoff model are presented. Such techniques are applied for forecasting the short-term future rainfall to be used as real-time input in a rainfall-runoff model and for updating the discharge predictions provided by the model. Along with traditional linear stochastic models, both...

Two important applications of rainfall-runoff models are forecasting and simulation. At present, rainfall-runoff models based on artificial intelligence methods are built basically for short-term forecasting purposes and these models are not very effective for simulation purposes. This study explores the applicability and effectiveness of adaptive neuro-fuzzy-system-based rainfall-runoff models...

The continuous occurrence of changes in the global climate causes significant variability in the seasonal and intra-seasonal rainfall pattern, which often leads to frequent floods and droughts in India. To reduce the magnitude of effect of such natural calamities and for better management of water resources, it is essential to predict the rainfall, well in advance. In this study the possible re...

Discharge events induced by mixture of snowmelt and rainfall are strongly nonlinear due to consequences of rain-on-snow phenomena and snowmelt dependence on energy balance. However, they received relatively little attention, especially in high-resolution discharge forecasting. In this study, we use Random Forests models for 24 h discharge forecasting in 1 h resolution in a 105.9 km2 urbanized c...

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...