arXiv:2607. 15775v1 Announce Type: cross Abstract: Access to potable water is crucial for health, economic development, and sustainability.
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arXiv:2609.13919v1 Announce Type: new Abstract: This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improvi...
arXiv:2606. 11268v1 Announce Type: new Abstract: Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs.
The paper introduces the Mass‑Conserving Perceptron (MCP), a physics‑aware AI framework that enforces conservation laws while learning hydrological process relationships from data. By progressively adding physically meaningful components—such as bounded soil storage, state‑dependent conductivity, variable porosity, infiltration capacity, surface ponding, vertical drainage, and nonlinear water‑table dynamics—to a single MCP storage unit, the authors demonstrate that predictive skill for daily streamflow improves across 15 U.S. catchments. The study finds that the impact of each process representation varies with hydroclimate, with vertical drainage boosting performance in arid and snow‑dominated basins but hindering it in rainfall‑dominated ones, while surface ponding has minimal effect; the best MCP configurations rival LSTM benchmarks while retaining explicit physical interpretability.
arXiv:2608. 19899v1 Announce Type: cross Abstract: Machine learning (ML) is widely used for the development of ocean colour algorithms, but most studies focus on model parameter training and hyperparameter tuning.
The paper introduces KAINN, a hybrid neural‑mechanistic model that augments the Agriculture‑informed Neural Network with domain knowledge on fertilizer diffusion, soil respiration, and water‑filled porosity to predict nitrous oxide emissions from agriculture. Experiments across CNN, LSTM, and Transformer architectures show that KAINN achieves lower root mean square error, lower mean absolute error, and higher R-squared values compared to purely data‑driven models and the original AINN. The learned interfaces exhibit smoother, more physically consistent parameter trajectories with reduced uncertainty.
The study evaluates how feature engineering (FE) affects machine learning models for ocean colour data, proposing a seven‑step optimisation framework that includes band selection, scaling, normalisation, index extraction, PCA, and feature scaling. Applied to Sentinel‑3 OLCI observations, the framework improves model accuracy for estimating Chlorophyll‑a and Secchi disk depth, achieving higher R values and lower mean absolute errors compared to standard algorithms. However, the optimal FE varies across targets and models, indicating that FE optimisation must be tailored to each application.