[HTML][HTML] Analytical hierarchy process for sustainable agriculture: An overview

A Kumar, S Pant - MethodsX, 2023 - Elsevier
United nation sustainable development goal two (UNSDG-2) aims to achieve the eradication
of hunger along with the assurance of food security for all by 2030. This cannot be achieved …

Machine learning techniques in landslide susceptibility mapping: a survey and a case study

T Kavzoglu, I Colkesen, EK Sahin - Landslides: Theory, practice and …, 2019 - Springer
Abstract Machine learning techniques have been increasingly employed for solving many
scientific and engineering problems. These data driven methods have been lately utilized …

Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea

WL Hakim, F Rezaie, AS Nur, M Panahi… - Journal of environmental …, 2022 - Elsevier
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 …

[HTML][HTML] Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia

AM Youssef, HR Pourghasemi - Geoscience Frontiers, 2021 - Elsevier
The current study aimed at evaluating the capabilities of seven advanced machine learning
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …

Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping

Y Wu, Y Ke, Z Chen, S Liang, H Zhao, H Hong - Catena, 2020 - Elsevier
Landslides are a common type of natural disaster that brings great threats to the human lives
and economic development around the world, especially in the Chinese Loess Plateau …

[HTML][HTML] GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

SA Ali, F Parvin, J Vojteková, R Costache, NTT Linh… - Geoscience …, 2021 - Elsevier
Hazards and disasters have always negative impacts on the way of life. Landslide is an
overwhelming natural as well as man-made disaster that causes loss of natural resources …

Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping

Z Fang, Y Wang, L Peng, H Hong - Computers & Geosciences, 2020 - Elsevier
Landslides are regarded as one of the most common geological hazards in a wide range of
geo-environment. The aim of this study is to assess landslide susceptibility by integrating …

Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques

MS Tehrany, S Jones, F Shabani - Catena, 2019 - Elsevier
River flooding can be a highly destructive natural hazard. Numerous approaches have been
used to study the phenomenon; however, insufficient knowledge regarding flood …

Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques

W Chen, HR Pourghasemi, A Kornejady, N Zhang - Geoderma, 2017 - Elsevier
Abstract “Spatial contraindication” is what exactly landslide susceptibility models have been
seeking. They are designed for depicting perilous land activities, be it natural or …

[HTML][HTML] Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms

AL Balogun, F Rezaie, QB Pham, L Gigović… - Geoscience …, 2021 - Elsevier
In this study, we developed multiple hybrid machine-learning models to address parameter
optimization limitations and enhance the spatial prediction of landslide susceptibility models …