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Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development
Abstract Accurate prediction of Land Surface Temperature (LST) is critical for understanding
and mitigating the effects of climate change and land use dynamics. This study proposes a …
and mitigating the effects of climate change and land use dynamics. This study proposes a …
Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review
Abstract The Gravity Recovery and Climate Experiment (GRACE) data has brought
significant advancements in groundwater (GW) analysis by providing crucial information …
significant advancements in groundwater (GW) analysis by providing crucial information …
Land use and land cover change detection using the random forest approach: the case of the upper Blue Nile River Basin, Ethiopia
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 …
and biodiversity loss on a global scale. This study is designed to classify land use and land …
A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data
X Cui, Z Wang, N Xu, J Wu, Z Yao - Environmental Modelling & Software, 2024 - Elsevier
Groundwater level (GWL) prediction is important for ecological protection and resource
utilization; it helps in formulating policies for artificial groundwater recharge, modifying the …
utilization; it helps in formulating policies for artificial groundwater recharge, modifying the …
Unlocking the hidden potential: groundwater zone map** using AHP, remote sensing and GIS techniques
RS Shelar, SB Nandgude, CB Pande… - … , Natural Hazards and …, 2023 - Taylor & Francis
Groundwater resource problems are increasing development and planning challenges due
to climate change and overexploitation, necessitating assessments of their potential and …
to climate change and overexploitation, necessitating assessments of their potential and …
Machine learning-based monitoring and design of managed aquifer rechargers for sustainable groundwater management: scope and challenges
Managed aquifer recharge (MAR) replenishes groundwater by artificially entering water into
subsurface aquifers. This technology improves water storage, reduces over-extraction, and …
subsurface aquifers. This technology improves water storage, reduces over-extraction, and …
[HTML][HTML] Drinking water resources suitability assessment based on pollution index of groundwater using improved explainable artificial intelligence
The global significance of fluoride and nitrate contamination in coastal areas cannot be
overstated, as these contaminants pose critical environmental and public health challenges …
overstated, as these contaminants pose critical environmental and public health challenges …
Forecasting of meteorological drought using ensemble and machine learning models
This study highlights drought forecasting for understanding the semi-arid area in India,
where drought phenomena play vital role in the irrigation, drinking water supplies, and …
where drought phenomena play vital role in the irrigation, drinking water supplies, and …
A novel implementation of pre-processing approaches and hybrid kernel-based model for short-and long-term groundwater drought forecasting
Groundwater drought, as a form of hydrological drought, embodies the distinctive
characteristics of the aquifer and human-induced disruptions within the hydrological system …
characteristics of the aquifer and human-induced disruptions within the hydrological system …
Building a mangrove ecosystem monitoring tool for managers using Sentinel-2 imagery in Google Earth Engine
Mangroves are among the most productive ecosystems worldwide, providing numerous
ecological and socio-economic co-benefits. Though highly adapted to fluctuating …
ecological and socio-economic co-benefits. Though highly adapted to fluctuating …