This study develops a late spring-early summer rainfall forecasting model using an artificial
neural network (ANN) for the Geum River Basin in South Korea. After identifying the lagged …

Global climate change is affecting water resources and other aspects of life in many
countries. Rainfall is the most significant climate element affecting the livelihood and well …

Water managers in the western United States (US) rely on longterm forecasts of temperature
and precipitation to prepare for droughts and other wet weather extremes. To improve the …

Summer rainfall in the Sahel region of Africa exhibits one of the largest signals of climatic
variability and with a population reliant on agricultural productivity, the Sahel is particularly …

Artificial intelligence (AI) methods have seen increasingly widespread use in everything from
consumer products and driverless cars to fraud detection and weather forecasting. The use …

Sub-seasonal forecasting (SSF) focuses on predicting key variables such as temperature
and precipitation on the 2-week to 2-month time scale. Skillful SSF would have immense …

In this study, four machine-learning (ML) models [gradient boost decision tree (GBDT), light
gradient boosting machine (LightGBM), categorical boosting (CatBoost), and extreme …

In this study, two machine learning (ML) models (support vector regression (SVR) and
extreme gradient boosting (XGBoost)) are developed to perform seasonal forecasts of the …

Water evaporating from the ocean sustains precipitation on land. This ocean-to-land
moisture transport leaves an imprint on sea surface salinity (SSS). Thus, the question arises …

General circulation models, which forecast by first modelling actual conditions in the
atmosphere and ocean, are used extensively for monthly rainfall forecasting. We show how …