[HTML][HTML] Flood susceptibility modelling using advanced ensemble machine learning models
ARMT Islam, S Talukdar, S Mahato, S Kundu… - Geoscience …, 2021 - Elsevier
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 …
A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020
J Huang, X Wu, S Ling, X Li, Y Wu, L Peng… - … Science and Pollution …, 2022 - Springer
To assess the status of hotspots and research trends on geographic information system
(GIS)–based landslide susceptibility (LS), we analysed 1142 articles from the Thomas …
(GIS)–based landslide susceptibility (LS), we analysed 1142 articles from the Thomas …
Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy
M Mehrabi - Natural Hazards, 2021 - Springer
This study deals with landslide susceptibility mapping in the northern part of Lecco Province,
Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen …
Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen …
GIS-based ensemble computational models for flood susceptibility prediction in the Quang Binh Province, Vietnam
Recently, floods are occurring more frequently every year around the world due to increased
anthropogenic activities and climate change. There is a need to develop accurate models for …
anthropogenic activities and climate change. There is a need to develop accurate models for …
Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier
M Sahana, BT Pham, M Shukla, R Costache… - Geocarto …, 2022 - Taylor & Francis
In this study, we have investigated rainfall induced landslide susceptibility of the Uttarkashi
district of India through the developmentof different novel GIS based soft computing …
district of India through the developmentof different novel GIS based soft computing …
Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region
Y Chen, W Chen, S Janizadeh, GS Bhunia… - Geocarto …, 2022 - Taylor & Francis
Piping erosion is one of the water erosions that cause significant changes in the landscape,
leading to environmental degradation. To prevent losses resulting from tube growth and …
leading to environmental degradation. To prevent losses resulting from tube growth and …
Landslide susceptibility mapping using an ensemble model of Bagging scheme and random subspace–based naïve Bayes tree in Zigui County of the Three Gorges …
X Hu, C Huang, H Mei, H Zhang - Bulletin of Engineering Geology and the …, 2021 - Springer
A novel machine learning ensemble model that is a hybridization of Bagging and random
subspace–based naïve Bayes tree (RSNBtree), named as BRSNBtree, was used to prepare …
subspace–based naïve Bayes tree (RSNBtree), named as BRSNBtree, was used to prepare …
[HTML][HTML] Spatial prediction of future flood risk: an approach to the effects of climate change
Preparation of a flood probability map serves as the first step in a flood management
program. This research develops a probability flood map for floods resulting from climate …
program. This research develops a probability flood map for floods resulting from climate …
Ensemble machine learning models based on Reduced Error Pruning Tree for prediction of rainfall-induced landslides
BT Pham, A Jaafari, T Nguyen-Thoi… - … Journal of Digital …, 2021 - Taylor & Francis
In this paper, we developed highly accurate ensemble machine learning models integrating
Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D) …
Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D) …
Optimization of statistical and machine learning hybrid models for groundwater potential mapping
Determining areas of high groundwater potential is important for exploitation, management,
and protection of water resources. This study assesses the spatial distribution of …
and protection of water resources. This study assesses the spatial distribution of …