Intelligent Modeling of Soil Moisture Variability Using Remote Sensing and Spiking Neural Networks

Soil moisture prediction requires the integration of multisource data, including satellite observations, ground-based sensors, and airborne systems, each contributing critical information for modeling Earth’s hydrological cycles. The complexity of this task necessitates an analytical framework capab...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Procedia computer science Ročník 270; s. 1372 - 1380
Hlavní autoři: El Maachi, Soukaina, Saadane, Rachid, Chehri, Abdellah
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 2025
Témata:
Artificial Intelligence
Climate change
climate data
Spiking neural networks
Climate change
climate data
Artificial Intelligence
Spiking neural networks
ISSN:1877-0509, 1877-0509
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Soil moisture prediction requires the integration of multisource data, including satellite observations, ground-based sensors, and airborne systems, each contributing critical information for modeling Earth’s hydrological cycles. The complexity of this task necessitates an analytical framework capable of reconciling general modeling principles with the intricate variability of climatic factors to ensure reliable predictions. This study explores the application of Spiking Neural Networks (SNNs) as an advanced approach beyond conventional methodologies, utilizing array-sensed data from the ERA5 dataset. SNNs are distinguished by their ability to merge computational efficiency with biologically inspired dynamics, employing Leaky Integrate-and-Fire neurons to process spatial and temporal information effectively. The model’s adaptability and precision in handling large-scale climatic datasets were evaluated using an 80-20 data split, achieving a Mean Squared Error (MSE) of 0.0003, an R2 value of 0.8919, and a Pearson correlation coefficient of 0.9449, reinforcing its predictive capability and ability to capture intrinsic dependencies within soil moisture dynamics. This novel implementation of SNNs enhances prediction accuracy while offering a computationally efficient solution for soil moisture forecasting, addressing key challenges in environmental and agricultural applications. The findings provide a foundation for future research aimed at optimizing hydrological models through biologically inspired neural architectures.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2025.09.258