With the increasing severe pollution, the new energy industry is greatly favored by the
government and investors. Using the static network connectedness method of Diebold and …

The applicability of four machine learning (ML) methods, ANFIS-PSO, ANFIS-FCM, MARS
and M5Tree, together with multi model simple averaging (MM-SA) ensemble method, is …

Prediction of river flow as a fundamental source of hydrological information plays a crucial
role in various fields of water projects. In this study, at first, the capabilities of two time series …

Modelling river water level (WL) of a coastal catchment is much complex due to the tidal
influences on river WL. A hybrid machine learning model based on relevance vector …

The capability of the extreme learning machine (ELM) model in modeling stochastic,
nonlinear, and complex hydrological engineering problems has been proven remarkably …

Drought modelling is an important issue because it is required for curbing or mitigating its
effects, alerting the people to the its consequences, and water resources planning. This …

River flow rates are important for water resources projects. Given this, the current study
explored the use of autoregressive (AR) and moving average (MA) techniques as individual …

The UK's energy policy aiming to reduce carbon emissions has effectively driven down the
use of coal in its electricity generation mix. But unless the power system can transition to a …

Ordinary least squares (OLS) regression offers a decision-oriented approach for modeling
trends in annual peak flows. We introduce a two-stage OLS approach for nonstationary flood …

Abstract The decline in Lake Urmia (LU) water level during the past two decades has been
addressed by several studies. However, the conducted studies could not come across a …