Version 1
: Received: 8 July 2024 / Approved: 8 July 2024 / Online: 9 July 2024 (14:05:26 CEST)
How to cite:
Badem, C.; Yılmaz, R.; Cesur, M. R.; Cesur, E. Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data. Preprints2024, 2024070648. https://doi.org/10.20944/preprints202407.0648.v1
Badem, C.; Yılmaz, R.; Cesur, M. R.; Cesur, E. Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data. Preprints 2024, 2024070648. https://doi.org/10.20944/preprints202407.0648.v1
Badem, C.; Yılmaz, R.; Cesur, M. R.; Cesur, E. Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data. Preprints2024, 2024070648. https://doi.org/10.20944/preprints202407.0648.v1
APA Style
Badem, C., Yılmaz, R., Cesur, M. R., & Cesur, E. (2024). Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data. Preprints. https://doi.org/10.20944/preprints202407.0648.v1
Chicago/Turabian Style
Badem, C., Muhammet Raşit Cesur and Elif Cesur. 2024 "Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data" Preprints. https://doi.org/10.20944/preprints202407.0648.v1
Abstract
Dams significantly impact the environment, industry, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely within the framework of Integrated Water Resource Management (IWRM). Our research proposes that combining the physical model of evapotranspiration using the Penman–Monteith equation with data-driven models based on historical reservoir data, weather data, and consumption data is essential for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted a statistical test on the MAPE values. We achieved a remarkable accuracy in predicting the occupancy level one month ahead, with an error margin of just 1% using Extra Trees. This study represents a pioneering effort in providing guidance and proposing a hybrid model in this field.
Keywords
Artificial Intelligence; Integrated Water Resource Management; Dam Occupancy Prediction
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
