Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022

DK Pandey, AI Hunjra, R Bhaskar, MAS Al-Faryan - Resources Policy, 2023 - Elsevier
Applying artificial intelligence (AI), machine learning (ML), and big data to natural resource
management (NRM) is revolutionizing how natural resources are managed. To gain more …

Selected AI optimization techniques and applications in geotechnical engineering

KC Onyelowe, FF Mojtahedi, AM Ebid… - Cogent …, 2023 - Taylor & Francis
In an age of depleting earth due to global warming impacting badly on the ozone layer of the
earth system, the need to employ technologies to substitute those engineering practices …

COVID-19 and urban vulnerability in India

SV Mishra, A Gayen, SM Haque - Habitat international, 2020 - Elsevier
The global pandemic has an inherently urban character. The UN-Habitat's publication of a
Response Plan for mollification of the SARS-CoV-2 based externalities in the cities of the …

Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility

W Chen, X Lei, R Chakrabortty, SC Pal… - Journal of …, 2021 - Elsevier
The objective of this study is to assess the gully head-cut erosion susceptibility and identify
gully erosion prone areas in the Meimand watershed, Iran. In recent years, this study area …

Comparative analysis of gradient boosting algorithms for landslide susceptibility map**

EK Sahin - Geocarto International, 2022 - Taylor & Francis
The aim of the study is to compare four recent gradient boosting algorithms named as
Gradient Boosting Machine (GBM), Categorical Boosting (CatBoost), Extreme Gradient …

Soil erosion susceptibility assessment using logistic regression, decision tree and random forest: study on the Mayurakshi river basin of Eastern India

A Ghosh, R Maiti - Environmental Earth Sciences, 2021 - Springer
Soil erosion is one of the major environmental hazards causing severe land degradation in
the sub-tropical monsoon dominated Mayurakshi river basin (MRB) of Eastern India. Hence …

Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River …

S Saha, M Saha, K Mukherjee, A Arabameri… - Science of the Total …, 2020 - Elsevier
Rapid population growth and its corresponding effects like the expansion of human
settlement, increasing agricultural land, and industry lead to the loss of forest area in most …

A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

A Arabameri, S Saha, J Roy, JP Tiefenbacher… - Science of the Total …, 2020 - Elsevier
Land subsidence (LS) is a significant problem that can cause loss of life, damage property,
and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a …

[HTML][HTML] Soil erosion susceptibility map** using a GIS-based multi-criteria decision approach: Case of district Chitral, Pakistan

B Aslam, A Maqsoom, WS Alaloul, MA Musarat… - Ain Shams Engineering …, 2021 - Elsevier
Soil erosion has serious threats to agricultural production, hydraulic structures, and the
world's ecosystem. The objective of this study is the delineation of soil erosion susceptibility …

Landslide susceptibility map** and driving mechanisms in a vulnerable region based on multiple machine learning models

H Yu, W Pei, J Zhang, G Chen - Remote Sensing, 2023 - mdpi.com
Landslides can cause severe damage to both the environment and society, and many
statistical, index-based, and inventory-based methods have been developed to assess …