[HTML][HTML] Artificial intelligence in agricultural mapping: A review

R Espinel, G Herrera-Franco, JL Rivadeneira García… - Agriculture, 2024 - mdpi.com
Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and
time and increases efficiency in agricultural management activities, which improves the food …

Bagging-based machine learning algorithms for landslide susceptibility modeling

T Zhang, Q Fu, H Wang, F Liu, H Wang, L Han - Natural hazards, 2022 - Springer
Landslide hazards have attracted increasing public attention over the past decades due to a
series of catastrophic consequences of landslide occurrence. Thus, the mitigation and …

Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment

VH Nhu, A Mohammadi, H Shahabi, BB Ahmad… - International journal of …, 2020 - mdpi.com
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 …

An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security

M Kazemi Garajeh, B Salmani, S Zare Naghadehi… - Scientific Reports, 2023 - nature.com
The agriculture sector provides the majority of food supplies, ensures food security, and
promotes sustainable development. Due to recent climate changes as well as trends in …

[HTML][HTML] Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14 …

HR Pourghasemi, S Pouyan, B Heidari… - International Journal of …, 2020 - Elsevier
Abstract Objectives Coronavirus disease 2019 (COVID-19) represents a major pandemic
threat that has spread to more than 212 countries with more than 432,902 recorded deaths …

Earth fissure hazard prediction using machine learning models

B Choubin, A Mosavi, EH Alamdarloo, FS Hosseini… - Environmental …, 2019 - Elsevier
Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the
semi-arid basins. The excessive withdrawal of groundwater, as well as the other …

Simulating wetland changes under different scenarios based on integrating the random forest and CLUE-S models: A case study of Wuhan Urban Agglomeration

K Peng, W Jiang, Y Deng, Y Liu, Z Wu, Z Chen - Ecological Indicators, 2020 - Elsevier
Wetlands are one of the most productive ecosystems and play an important role in
supporting a wide range of biodiversity and providing various kinds of ecosystem services …

[HTML][HTML] Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

HR Pourghasemi, N Sadhasivam, N Kariminejad… - Geoscience …, 2020 - Elsevier
This investigation assessed the efficacy of 10 widely used machine learning algorithms
(MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized …

Groundwater potential mapping in hubei region of china using machine learning, ensemble learning, deep learning and automl methods

Z Bai, Q Liu, Y Liu - Natural Resources Research, 2022 - Springer
Freshwater scarcity has become more widespread on a global scale in recent years. Surface
water resources are no longer sufficient to meet the demands of human productivity and …

Land subsidence susceptibility mapping in jakarta using functional and meta-ensemble machine learning algorithm based on time-series InSAR data

WL Hakim, AR Achmad, CW Lee - Remote Sensing, 2020 - mdpi.com
Areas at risk of land subsidence in Jakarta can be identified using a land subsidence
susceptibility map. This study evaluates the quality of a susceptibility map made using …