With the continuous increase in global photovoltaic installations, the importance of
photovoltaic power generation to the power industry has gradually increased, which means …

Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting
publications has been recorded. Both statistical and artificial intelligence (AI) approaches …

We introduce a data-driven forecasting method for high-dimensional chaotic systems using
long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural …

Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing
to the high number of interrelated hydrological processes. It is well-known that machine …

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 …

Accurately modeling rainfall–runoff (R–R) transform remains a challenging task despite that
a wide range of modeling techniques, either knowledge-driven or data-driven, have been …

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 …

In this paper, the accuracy performance of monthly streamflow forecasts is discussed when
using data‐driven modeling techniques on the streamflow series. A crisp distributed support …

India is facing the worst water crisis in its history and major Indian cities which accommodate
about 50% of its population will be among highly groundwater stressed cities by 2020. In …

In this paper, three data-preprocessing techniques, moving average (MA), singular spectrum
analysis (SSA), and wavelet multi-resolution analysis (WMRA), were coupled with artificial …