Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the …
The Damoh district, which is located in the central India and characterized by limestone,
shales, and sandstone compact rock. The district has been facing groundwater development …
shales, and sandstone compact rock. The district has been facing groundwater development …
Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea
Landslides are a geological hazard that can pose a serious threat to human health and the
environment of highlands or mountain slopes. Landslide susceptibility mapping is an …
environment of highlands or mountain slopes. Landslide susceptibility mapping is an …
Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms
Groundwater potential mapping (GPM) provides the valuable information on groundwater
volume that can be withdrawn from the aquifer without affecting the environmental …
volume that can be withdrawn from the aquifer without affecting the environmental …
Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: case study of Shiraz plain
H Moayedi, M Salari, AA Dehrashid, BN Le - … Environmental Research and …, 2023 - Springer
In recent decades, qualitative and quantitative assessments of groundwater sources reveal
that efficient and accurate optimization approaches may assist in solving the multiple …
that efficient and accurate optimization approaches may assist in solving the multiple …
Global Review of Modification, optimization, and improvement models for Aquifer Vulnerability Assessment in the era of Climate Change
Abstract Purpose of Review This review aims to examine the methods used to date in
assessing aquifer vulnerability over the last three decades (1993-2023). In addition to a …
assessing aquifer vulnerability over the last three decades (1993-2023). In addition to a …
[HTML][HTML] Convolutional neural network and long short-term memory algorithms for groundwater potential mapping in Anseong, South Korea
Study region The study area was the Anseong-si area that located in the southernmost part
of Gyeonggi-do Province at 127° 19′ E, 36° 82′ N. Anseong has a transitional climate …
of Gyeonggi-do Province at 127° 19′ E, 36° 82′ N. Anseong has a transitional climate …
Spatial prediction of groundwater potentiality using machine learning methods with Grey Wolf and Sparrow Search Algorithms
R Liu, G Li, L Wei, Y Xu, X Gou, S Luo, X Yang - Journal of Hydrology, 2022 - Elsevier
Groundwater is considered to be one of the most valuable natural resources in the world.
However, the availability of groundwater is of concern. Therefore, understanding the …
However, the availability of groundwater is of concern. Therefore, understanding the …
Spatial prediction of wildfire susceptibility using hybrid machine learning models based on support vector regression in sydney, australia
Australia has suffered devastating wildfires recently, and is predisposed to them due to
several factors, including topography, meteorology, vegetation, and ignition sources. This …
several factors, including topography, meteorology, vegetation, and ignition sources. This …
Naïve Bayes ensemble models for groundwater potential mapping
Groundwater potential maps are important tools for the sustainable management of water
resources, especially in agricultural producing countries like Vietnam. Here, we describe the …
resources, especially in agricultural producing countries like Vietnam. Here, we describe the …
Novel hybrid models to enhance the efficiency of groundwater potentiality model
The present study aimed to create novel hybrid models to produce groundwater potentiality
models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning …
models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning …