Machine learning in geo-and environmental sciences: From small to large scale
In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
Global review of groundwater potential models in the last decade: parameters, model techniques, and validation
NN Thanh, P Thunyawatcharakul, NH Ngu… - Journal of …, 2022 - Elsevier
This paper aims to review parameters, model techniques, validation methods in
groundwater potential field. According to statistics, there are three major model groups used …
groundwater potential field. According to statistics, there are three major model groups used …
Critical role of climate factors for groundwater potential map** in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms
Improving prediction of water quality indices using novel hybrid machine-learning algorithms
River water quality assessment is one of the most important tasks to enhance water
resources management plans. A water quality index (WQI) considers several water quality …
resources management plans. A water quality index (WQI) considers several water quality …
Spatial prediction of groundwater potential map** based on convolutional neural network (CNN) and support vector regression (SVR)
Freshwater shortages have become much more common globally in recent years. Water
resources that are naturally available beneath the surface are capable of reversing this …
resources that are naturally available beneath the surface are capable of reversing this …
Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques
The quality of digital elevation models (DEMs), as well as their spatial resolution, are
important issues in geomorphic studies. However, their influence on landslide susceptibility …
important issues in geomorphic studies. However, their influence on landslide susceptibility …
Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods
Floods are one of the most devastating types of disasters that cause loss of lives and
property worldwide each year. This study aimed to evaluate and compare the prediction …
property worldwide each year. This study aimed to evaluate and compare the prediction …
Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles
Flooding is a very common natural hazard that causes catastrophic effects worldwide.
Recently, ensemble-based techniques have become popular in flood susceptibility …
Recently, ensemble-based techniques have become popular in flood susceptibility …
Flood susceptibility map** in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic …
Flooding is one of the most significant environmental challenges and can easily cause fatal
incidents and economic losses. Flood reduction is costly and time-consuming task; so it is …
incidents and economic losses. Flood reduction is costly and time-consuming task; so it is …
Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran
Iran experiences frequent destructive floods with significant socioeconomic consequences.
Quantifying the accurate impacts of such natural hazards, however, is a complicated task …
Quantifying the accurate impacts of such natural hazards, however, is a complicated task …