Several industries, including agriculture, water resource management, disaster preparedness, and urban planning, depend heavily on rainfall forecasting. Traditional approaches for predicting rely numerical simulations statistical models, which frequently have accuracy computing efficiency issues. Growing interest has been shown in using machine learning (ML) algorithms to enhance prediction as ...

introduction: rainfall is affected by changes in the global sea level change, especially changes in sea surface temperature sst sea surface temperature and sea level pressure slp sea level pressure. climate anomalies being related to each other at large distance is called teleconnection. as physical relationships between rainfall and teleconnection patterns are not defined clearly, we used inte...

Taiwan’s special climate and landforms are affected by summer typhoons, with 78% of its rainfall occurring during the summer and autumn months. The range and the severity of disasters has increased in recent years, thanks in part to climate change, which has caused an unstable rainfall. Accurate rainfall predictions help to forecast rivers’ water levels. This study proposes a new rainfall predi...

Extreme climatic events, such as flooding rains, extended decadal droughts and heat waves have been identified increasingly as important regulators of natural populations. Climate models predict that global warming will drive changes in rainfall and increase the frequency and severity of extreme events. Consequently, to anticipate how organisms will respond we need to document how changes in ex...

General circulation models (GCMs), used to predict rainfall at a seasonal lead-time, tend to simulate too many rainfall events of too low intensity relative to individual stations within a GCM grid cell. Even if bias in total rainfall is corrected relative to a target location, this distortion of frequency and intensity is expected to adversely affect simulations of crop growth and yield. We pr...

The Institute for Soil, Climate and Water developed an early warning and decision support system funded by the National Department of Agriculture. The system went operational in October 2000. The system is refered to as Umlindi ( Zulu word for the watchman) and provides information on fire, vegetation condition, water satisfaction index (crop yield prediction and drought monitoring model) and r...