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hybrid-modeling

topic2 events
papersTODAY 04:00 UTC

Hybrid Machine-Learning Model Adds Uncertainty Estimates to Irrigation Scheduling

A new arXiv paper presents a hybrid mathematical and machine-learning approach for irrigation decision support that also quantifies confidence in its soil-moisture forecasts. The authors note that irrigation is typically scheduled reactively, even though agriculture uses about 70% of global freshwater withdrawals. The model aims to give growers both a forward-looking moisture prediction and a measure of how reliable that prediction is.

research/agriculture/uncertainty/forecasting
papersSEP 12 04:00 UTC

arXiv paper integrates rainfall data into water-quality forecasting models

A revised arXiv preprint proposes a method for learning intrinsic water-quality dynamics that incorporates rainfall as an environmental driver. Rainfall affects water quality through runoff, pollutant transport, dilution and resuspension, processes that mechanistic models describe explicitly but which are hard to capture in purely data-driven approaches. The work aims to combine these perspectives for improved forecasting.

water-quality/forecasting/arxiv/environment