Incorporating slope-unit morphometric summaries and lithological proportions into pixel-based models modestly improves predictive accuracy and reduces map fragmentation in landslide ...
Researchers developed a two-stage deep learning framework that reduces systematic errors in coastal ocean temperature reconstructions using satellite data and ...
Spatial distribution modeling projects that climate change will drive a net contraction of up to 69.35 percent of highly suitable ...
An artificial neural network framework downscaled Australian FLDAS soil moisture to 500-meter resolution while preserving the parent product's temporal accuracy.
A comprehensive systematic review of more than 230 studies reveals that while artificial intelligence enhances groundwater mapping accuracy, real-world adoption ...
A comprehensive modeling study in Madhya Pradesh reveals that gradient-boosting ensembles outperform complex deep neural architectures in regional wildfire susceptibility ...
A matched satellite analysis of California's 2020 Creek Fire found increased snow persistence only in the most severely burned areas, ...
A study in coastal India reveals that spatial cross-validation exposes major performance limits in graph neural networks for road disruption ...
A new study combines machine learning and spatial explainable AI to predict surface urban heat island intensity across the Mumbai ...
An ensemble machine learning study of Dhaka identifies rainfall, elevation and road density as the dominant drivers of waterlogging susceptibility ...
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