Variations in rainfall negatively affect crop productivity and impose severe climatic
conditions in developing regions. Studies that focus on climatic variations such as variability …

In this paper, several soft computing approaches were employed for rainfall prediction. Two
aspects were considered to improve the accuracy of rainfall prediction:(1) carrying out a data …

This study is an attempt to seek a relatively optimal data-driven model for rainfall forecasting
from three aspects: model inputs, modeling methods, and data-preprocessing techniques …

Three artificial intelligence approaches-K-nearest neighbor (KNN), artificial neural network
(ANN), and extreme learning machine (ELM)-are used for the seasonal forecasting of …

The inter-annual and spatial variability of different rainfall variables is analysed over
Equatorial East Africa (Kenya and northeastern Tanzania). At the station level, three …

The present study aimed to develop a hybrid model to predict the rainfall time series of
Urmia Lake watershed. For this purpose, a model based on discrete wavelet transform …

Precipitation, as an important indicator describing the evolution of the regional climate
system, plays an important role in understanding the spatial and temporal distribution …

Forecasting precipitation is a crucial input to hydrological models and hydrological event
management. Accurate forecasts minimize the impact of extreme events on communities …

Modelling and forecasting of rainfall behaviour are of essential importance in various
hydrological and meteorological studies, which depends largely on properly understanding …

Proper understanding of rainfall variability is of essential importance for meteorological and
hydrological modelling. This study examines whether a variety of nonlinear dynamic …