Supply chain network design with financial considerations: A comprehensive review

H Jahani, B Abbasi, JB Sheu, W Klibi - European Journal of Operational …, 2024 - Elsevier
Supply chain network design (SCND) is a large and growing area of research. Although
SCND has a remarkable effect on companies' main financial accounts, our examination …

Can soil piping impact environment and society? Identifying new research gaps

A Bernatek‐Jakiel… - Earth Surface Processes …, 2023 - Wiley Online Library
Soil piping is a widespread, although often overlooked land degradation process. So far,
subsurface soil erosion studies have been focused on the importance of soil piping in …

[HTML][HTML] Comparison of machine learning models for gully erosion susceptibility mapping

A Arabameri, W Chen, M Loche, X Zhao, Y Li… - Geoscience …, 2020 - Elsevier
Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,
especially in the Northern provinces. A number of studies have been recently undertaken to …

Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia

O Rahmati, F Falah, KS Dayal, RC Deo… - Science of the total …, 2020 - Elsevier
A quantitative understanding of the hydro-environmental factors that influence the
occurrence of agricultural drought events would enable more strategic climate change …

Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities

O Rahmati, A Golkarian, T Biggs, S Keesstra… - Journal of …, 2019 - Elsevier
Land subsidence caused by land use change and overexploitation of groundwater is an
example of mismanagement of natural resources, yet subsidence remains difficult to predict …

An ensemble approach of feature selection and machine learning models for regional landslide susceptibility mapping in the arid mountainous terrain of Southern …

C Kumar, G Walton, P Santi, C Luza - Remote Sensing, 2023 - mdpi.com
This study evaluates the utility of the ensemble framework of feature selection and machine
learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic …

Novel machine learning approaches for modelling the gully erosion susceptibility

A Arabameri, O Asadi Nalivan, S Chandra Pal… - Remote Sensing, 2020 - mdpi.com
The extreme form of land degradation caused by the formation of gullies is a major
challenge for the sustainability of land resources. This problem is more vulnerable in the arid …

Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates

M Panahi, O Rahmati, F Rezaie, S Lee, F Mohammadi… - Catena, 2022 - Elsevier
Landslide susceptibility (LS) mapping is an essential tool for landslide risk assessment. This
study aimed to provide a new approach with better performance for landslide mapping and …

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 …

Performance evaluation for four GIS-based models purposed to predict and map landslide susceptibility: A case study at a World Heritage site in Southwest China

Y Jiao, D Zhao, Y Ding, Y Liu, Q Xu, Y Qiu, C Liu, Z Liu… - Catena, 2019 - Elsevier
Landslide susceptibility mapping is a prerequisite for preventing and mitigating hazardous
risks, especially in landslide-prone mountainous areas. This study applies two well-known …