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Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka

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  • Namal Rathnayake
  • Upaka Rathnayake
  • Tuan Linh Dang
  • Yukinobu Hoshino

Abstract

Hydrologic models to simulate river flows are computationally costly. In addition to the precipitation and other meteorological time series, catchment characteristics, including soil data, land use, land cover, and roughness, are essential in most hydrologic models. The unavailability of these data series challenged the accuracy of simulations. However, recent advances in soft computing techniques offer better approaches and solutions at less computational complexity. These require a minimum amount of data, while they reach higher accuracies depending on the quality of data sets. The Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference System (ANFIS) are two such systems that can be used in simulating river flows based on the catchment rainfall. In this paper, the computational capabilities of these two systems were tested in simulated river flows by developing the prediction models for Malwathu Oya in Sri Lanka. The simulated flows were then compared with the ground-measured river flows for accuracy. Correlation of coefficient (R), Per cent-Bias (bias), Nash Sutcliffe Model efficiency (NSE), Mean Absolute Relative Error (MARE), Kling-Gupta Efficiency (KGE), and Root mean square error (RMSE) were used as the comparative indices between Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference Systems. Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). The CatBoost algorithm outperformed other algorithms used in this study, with the best correlation score for the testing dataset having 0.9934. The extreme gradient boosting (XGBoost), Light gradient boosting (LightGBM), and Ensemble models scored 0.9283, 0.9253, and 0.9109, respectively. However, more applications should be investigated for sound conclusions.

Suggested Citation

  • Namal Rathnayake & Upaka Rathnayake & Tuan Linh Dang & Yukinobu Hoshino, 2023. "Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-21, April.
    • Handle: RePEc:plo:pone00:0282847
    DOI: 10.1371/journal.pone.0282847
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    Cited by:

    1. Wen-Dar Guo & Wei-Bo Chen & Chih-Hsin Chang, 2025. "A spatiotemporal watershed-scale machine-learning model for hourly and daily flood-water level prediction: the case of the tidal Beigang River, Taiwan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(8), pages 9563-9611, May.
    2. Sherin Kularathne & Amanda Perera & Namal Rathnayake & Upaka Rathnayake & Yukinobu Hoshino, 2024. "Analyzing the impact of socioeconomic indicators on gender inequality in Sri Lanka: A machine learning-based approach," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-25, December.
    3. Sherin Kularathne & Namal Rathnayake & Madhawa Herath & Upaka Rathnayake & Yukinobu Hoshino, 2024. "Impact of economic indicators on rice production: A machine learning approach in Sri Lanka," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-20, June.

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