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Novel Hybrid Machine Learning Algorithms for Lakes Evaporation and Power Production using Floating Semitransparent Polymer Solar Cells

Author

Listed:
  • Ismail Abd-Elaty

    (Zagazig University)

  • N. L. Kushwaha

    (ICAR-Indian Agricultural Research Institute)

  • Abhishek Patel

    (ICAR-Central Arid Zone Research Institute, Regional Research Station)

Abstract

The present study predicts the future evaporation losses by applying novel hybrid Machine Learning Algorithms (MLA). Water resources management is achieved by covering the reservoir water surface with floating semitransparent polymer solar cells. The energy produced by these panels will be used in the irrigation activities. The study is applied for the mass water body of Nasser Lake, Egypt and Sudan. Five MLAs namely additive regression (AR), AR-random subspace (AR-RSS), AR-M5Pruned (AR-M5P), AR-reduced error pruning tree (AR-REPTree), and AR- support vector machine (AR-SVM) were developed and evaluated for predicting future evaporation losses in the years 2030, 2050, and 2070. The study concludes that the hybrid AR-M5P ML model was not only superior to the AR model alone but also outperformed other hybrid models such as AR-RSS and AR-REPTree. The expected total annual water saving are projected to reach 3.47 billion cubic meters (BCM), 3.68 and 3.90 BCM, while the total annual power production is observed to be 1389 × 109 Megawatt (MW), 1535 × 109 MW and 1795 × 109 MW in the years 2030, 2050 and 2070, respectively. These results were achieved by covering the shallow water depths from contour level 0 m to 10 m below the surface water level. Additionally, this study shows the ability of using MLAs in the estimation of reservoir evaporation and addressing the water shortages in high stress regions. Graphical Abstract

Suggested Citation

  • Ismail Abd-Elaty & N. L. Kushwaha & Abhishek Patel, 2023. "Novel Hybrid Machine Learning Algorithms for Lakes Evaporation and Power Production using Floating Semitransparent Polymer Solar Cells," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4639-4661, September.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:12:d:10.1007_s11269-023-03565-2
    DOI: 10.1007/s11269-023-03565-2
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    References listed on IDEAS

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    1. Jayashree T R & NV Subba Reddy & U Dinesh Acharya, 2023. "Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1013-1032, February.
    2. Rafael M. Almeida & Rafael Schmitt & Steven M. Grodsky & Alexander S. Flecker & Carla P. Gomes & Lu Zhao & Haohui Liu & Nathan Barros & Rafael Kelman & Peter B. McIntyre, 2022. "Floating solar power could help fight climate change — let’s get it right," Nature, Nature, vol. 606(7913), pages 246-249, June.
    3. Pedro Beça & António C. Rodrigues & João P. Nunes & Paulo Diogo & Babar Mujtaba, 2023. "Optimizing Reservoir Water Management in a Changing Climate," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3423-3437, July.
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