Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …

Recently, there has been a notable tendency towards employing ensemble learning
methodologies in assorted areas of engineering, such as hydrology, for simulation and …

Digital transformation refers to the unprecedented disruptions in society, industry, and
organizations stimulated by advances in digital technologies such as artificial intelligence …

Feature selection becomes prominent, especially in the data sets with many variables and
features. It will eliminate unimportant variables and improve the accuracy as well as the …

Flood hazard map** (FHM) has undergone significant development in terms of approach
and capacity of the result to meet the target of policymakers for accurate prediction and …

Floods are one of nature's most destructive disasters because of the immense damage to
land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to …

Historical exploration of flash flood events and producing flash-flood susceptibility maps are
crucial steps for decision makers in disaster management. In this article, classification and …

This article is the first to analyze empirically the impact of digitalization on environmental
performance, using a database of 25 European countries over the period 2015–2020. We …

In recent years, the number of floods around the world has increased. As a result, Flood
Susceptibility Maps (FSMs) became vital for flood prevention, risk mitigation, and decision …

The main objective of the current study was to introduce a Deep Learning Neural Network
(DLNN) model in landslide susceptibility assessments and compare its predictive …