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Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data

Author

Listed:
  • Ahmet Cemkut Badem

    (Business School, Kocaeli University, Kocaeli 41001, Turkey)

  • Recep Yılmaz

    (Business School, Sakarya University, Sakarya 54050, Turkey)

  • Muhammet Raşit Cesur

    (Industrial Engineering Department, Istanbul Medeniyet University, Istanbul 34700, Turkey)

  • Elif Cesur

    (Industrial Engineering Department, Istanbul Medeniyet University, Istanbul 34700, Turkey)

Abstract

Dams significantly impact the environment, industries, 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 to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow “such as evapotranspiration using the Penman–Monteith equation, along with parameters like water consumption, solar radiation, and rainfall” with data-driven models based on historical reservoir data is crucial 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 statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature.

Suggested Citation

  • Ahmet Cemkut Badem & Recep Yılmaz & Muhammet Raşit Cesur & Elif Cesur, 2024. "Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data," Sustainability, MDPI, vol. 16(17), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7696-:d:1471421
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    References listed on IDEAS

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    1. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4113-4113, September.
    2. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3803-3823, August.
    3. Yashon O. Ouma & Ditiro B. Moalafhi & George Anderson & Boipuso Nkwae & Phillimon Odirile & Bhagabat P. Parida & Jiaguo Qi, 2022. "Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors," Sustainability, MDPI, vol. 14(22), pages 1-31, November.
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