The recent hype about artificial intelligence has sparked renewed interest in applying the
successful deep learning (DL) methods for image recognition, speech recognition, robotics …

Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …

Precipitation in any form—such as rain, snow, and hail—can affect day-to-day outdoor
activities. Rainfall prediction is one of the challenging tasks in weather forecasting process …

Water received in rainfall is a crucial natural resource for agriculture, the hydrological cycle,
and municipal purposes. The changing rainfall pattern is an essential aspect of assessing …

Accurate and reliable water resources planning and management to ensure sustainable use
of watershed resources cannot be achieved without precise and reliable models …

This study compares LSTM neural network and wavelet neural network (WNN) for spatio-
temporal prediction of rainfall and runoff time-series trends in scarcely gauged hydrologic …

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and
managing water resources. In this study, new generation deep learning models, recurrent …

Accurate and timely rainfall prediction is very important in hydrological modeling. Various
prediction methods have been proposed in recent years. In this work, information regarding …

Time-series analyses of temperature data are important for investigating temperature
variation and predicting temperature change. Here, Mann–Kendall (M–K) analyses of …

In this study, a new hybrid model integrated adaptive neuro fuzzy inference system with
Firefly Optimization algorithm (ANFIS-FFA), is proposed for forecasting monthly rainfall with …