Land use and land cover change detection using the random forest approach: The case of the Upper Blue Nile River basin, Ethiopia

BG Tikuye, M Rusnak, BR Manjunatha… - Global …, 2023 - Wiley Online Library
Monitoring land use change dynamics is critical for tackling food security, climate change,
and biodiversity loss on a global scale. This study is designed to classify land use and land …

Sentinel-1 SAR images and deep learning for water body mapping

F Pech-May, R Aquino-Santos, J Delgadillo-Partida - Remote Sensing, 2023 - mdpi.com
Floods occur throughout the world and are becoming increasingly frequent and dangerous.
This is due to different factors, among which climate change and land use stand out. In …

Land use land cover mapping and snow cover detection in himalayan region using machine learning and multispectral sentinel-2 satellite imagery

R Saini, S Singh - International Journal of Information Technology, 2024 - Springer
Abstract The Chamoli district of Uttarakhand, India, was recently devastated by a natural
disaster ie flash flood (07 February 2021), causing significant damage to life, property …

Land-Use Transitions and Its Driving Mechanism Analysis in Putian City, China, during 2000–2020

Q Peng, D Wu, W Lin, S Fan, K Su - Sustainability, 2024 - mdpi.com
Investigating the spatial-temporal evolution of land use and its driving forces provides a
scientific basis for policy formulation, land-use structure adjustment, and ecological …

[HTML][HTML] Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation …

S Ibrahim, H Balzter - Remote Sensing, 2024 - mdpi.com
Floods are significant global disasters, but their impact in developing countries is greater
due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor …

Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

Y Bammou, B Benzougagh, B Igmoullan, A Ouallali… - Natural Hazards, 2024 - Springer
This study explores and compares the predictive capabilities of various ensemble
algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood …

Dynamics changes of coastal aquaculture ponds based on the Google Earth Engine in Jiangsu Province, China

X Li, P Zhao, M Liang, X Ji, D Zhang, Z Xie - Marine Pollution Bulletin, 2024 - Elsevier
Monitoring the spatiotemporal variation in coastal aquaculture zones is essential to
providing a scientific basis for formulating scientifically reasonable land management …

[HTML][HTML] Machine learning screening tools for the prediction of extraction yields of pharmaceutical compounds from wastewaters

A Casas, D Rodríguez-Llorente… - Journal of Water …, 2024 - Elsevier
Pharmaceutical compounds have become an increasingly important source of pollutants in
wastewaters being conventional treatments ineffective in removing them, so they are …

A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh

ME Chowdhury, AKMS Islam, RU Zzaman… - Advances in Space …, 2024 - Elsevier
Flash floods are catastrophic global events, especially in northeast Bangladesh, and
assessing flash flood susceptibility is crucial for preparedness and mitigation. Traditional …

A deep learning-based approach to predict the flood patterns using Sentinel-1A time series images

M Siddique, T Ahmed, MS Husain - Journal of the Indian Society of …, 2024 - Springer
The risk from natural disasters is incessantly growing with the complex nature of advancing
globalization. The escalating change in global climate is a driving factor in increasing floods …