Prediction of spatial-temporal flood water level in agricultural fields using advanced machine learning and deep learning approaches

Adisa Hammed Akinsoji (), Bashir Adelodun (), Qudus Adeyi (), Rahmon Abiodun Salau (), Golden Odey () and Kyung Sook Choi ()
Additional contact information
Adisa Hammed Akinsoji: Kyungpook National University
Bashir Adelodun: Kyungpook National University
Qudus Adeyi: Kyungpook National University
Rahmon Abiodun Salau: Kyungpook National University
Golden Odey: Kyungpook National University
Kyung Sook Choi: Kyungpook National University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 7, No 5, 7915-7940

Abstract: Abstract Agricultural fields frequently experience flood disasters and significantly impacting food security, thus prompting the urgent need for efficient predictive flood mitigation mechanisms. This study presents an innovative approach for predicting spatial-temporal water levels in an agricultural field. Five ensemble machine-learning algorithms were developed to predict temporal channel water levels at four gauging points (GPs). Further, the ensemble Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM), a deep learning-based model was employed for spatial prediction – surface water level. The models were trained and validated using observed rainfall and simulated water level data for both drainage and field surfaces derived from SWMM-based hydrological models. The Random Forest and Extra trees models achieved superior performance in temporal predictions at gauging points 1 and 4, achieving R² and KGE values greater than 0.800. For the spatial inundation predictions, the RNN-LSTM achieved R2 and RMSE values of 0.999 and 0.094, respectively. This study underscores the critical influence of drainage network characteristics and design rainfall patterns in enhancing flood prediction accuracy. These results demonstrate the potential for precise flood prediction in agricultural fields and suggest the integration of machine learning and deep learning models into flood control and a decision support system, thereby enhancing flood management, decision-making, and preparedness against flood disasters.

Keywords: Flood prediction; Agricultural field; Machine learning; Flood disaster reduction (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-025-07118-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:7:d:10.1007_s11069-025-07118-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-025-07118-1

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().