Machine learning in environmental research: common pitfalls and best practices
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 …
sets and decipher complex relationships between system variables. However, due to the …
Ensemble machine learning paradigms in hydrology: A review
Recently, there has been a notable tendency towards employing ensemble learning
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
Digital transformation and environmental sustainability: A review and research agenda
Digital transformation refers to the unprecedented disruptions in society, industry, and
organizations stimulated by advances in digital technologies such as artificial intelligence …
organizations stimulated by advances in digital technologies such as artificial intelligence …
Selecting critical features for data classification based on machine learning methods
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 …
features. It will eliminate unimportant variables and improve the accuracy as well as the …
[HTML][HTML] Flood susceptibility modelling using advanced ensemble machine learning models
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 …
land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to …
Flood hazard map** methods: A review
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 …
and capacity of the result to meet the target of policymakers for accurate prediction and …
Is digitalization a driver to enhance environmental performance? An empirical investigation of European countries
TTL Huong, TT Thanh - Sustainable Production and Consumption, 2022 - Elsevier
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 …
performance, using a database of 25 European countries over the period 2015–2020. We …
Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
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 …
(DLNN) model in landslide susceptibility assessments and compare its predictive …
Flash-flood susceptibility map** based on XGBoost, random forest and boosted regression trees
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 …
crucial steps for decision makers in disaster management. In this article, classification and …
[HTML][HTML] Predicting flood susceptibility using LSTM neural networks
Identifying floods and producing flood susceptibility maps are crucial steps for decision-
makers to prevent and manage disasters. Plenty of studies have used machine learning …
makers to prevent and manage disasters. Plenty of studies have used machine learning …