arXiv Machine Learning June 18, 2026 By Ilektra Tsimpidi, George Georgoulas, Vidya Sumathy, George Nikolakopoulos

A Survey on Data-Driven Models for Soil Moisture Regression and Classification

Read the original on arXiv Machine Learning →

arXiv:2606. 18316v1 Announce Type: new Abstract: Soil Moisture (SM) modelling constitutes a complex spatiotemporal learning problem characterised by nonlinear environmental interactions, heterogeneous data sources, and limited ground observations.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 14

Parameter estimation for land-surface models using Neural Physics

arXiv:2505. 02979v4 Announce Type: replace-cross Abstract: We propose a novel inverse-modelling approach that estimates the parameters of a simple land-surface model (LSM) by assimilating data into a differentiable, physics-based forward model formulated using convolutional operations.

By Ruiyue Huang, Claire E. Heaney, Maarten van Reeuwijk

arXiv AI

Jun 4

Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

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arXiv Machine Learning

Jun 5

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming

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By Bosong Zhang, Timothy M. Merlis

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arXiv AI

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A Responsible Artificial Intelligence Framework for Groundwater Modeling

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By Chong Chen, Yulu Zhang, Qingxi Guo, Yihan Liu

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arXiv Machine Learning

Jun 9

Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning

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By Tewodros Alemu Ayall, Andy Li, Matthew Beddows, Milan Markovic, Georgios Leontidis

arXiv AI

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Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector

arXiv:2607. 10208v1 Announce Type: cross Abstract: Accurate meteorological forecasting is essential for agricultural planning, irrigation management, and environmental decision support.

By Piotr Sikora, Sotirios Kontogiannis

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