[HTML][HTML] Groundwater level prediction using machine learning models: A comprehensive review
Developing accurate soft computing methods for groundwater level (GWL) forecasting is
essential for enhancing the planning and management of water resources. Over the past two …
essential for enhancing the planning and management of water resources. Over the past two …
Influence of data splitting on performance of machine learning models in prediction of shear strength of soil
The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
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 …
GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models
W Chen, Y Li - Catena, 2020 - Elsevier
Landslides have caused huge economic and human losses in China. Mapping of landslide
susceptibility is an important tool to prevent and control landslide disasters. The purpose of …
susceptibility is an important tool to prevent and control landslide disasters. The purpose of …
[HTML][HTML] Performance evaluation of machine learning methods for forest fire modeling and prediction
Predicting and mapping fire susceptibility is a top research priority in fire-prone forests
worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB) …
worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB) …
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volume that can be withdrawn from the aquifer without affecting the environmental …
volume that can be withdrawn from the aquifer without affecting the environmental …
Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential
Y Chen, W Chen, S Chandra Pal, A Saha… - Geocarto …, 2022 - Taylor & Francis
Delineation of the groundwater's potential zones is a growing phenomenon worldwide due
to the high demand for fresh groundwater. Therefore, the identification of potential …
to the high demand for fresh groundwater. Therefore, the identification of potential …
Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping
Using multiple ensemble learning techniques for improving the predictive accuracy of
landslide models is an active research area. In this study, we combined a radial basis …
landslide models is an active research area. In this study, we combined a radial basis …
[HTML][HTML] Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an
ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands …
ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands …
[HTML][HTML] Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques
Study region The present study has been carried out in the Tabriz River basin (5397 km 2) in
north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles …
north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles …