From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh

Md. Abu Saleh, H.M. Rasel, Briti Ray
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Abstract

River discharge forecasting stands at the forefront of environmental management, contributing significantly to sustainable development through its impact on flood prevention, water resource management, ecological conservation, and energy production. This study forecasted the annual river discharge forecasting in the Nilphamari district of Bangladesh, employing random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM) techniques. Historical river discharge data spanning from 1990 to 2020, obtained from eight surface water stations, forms the basis of the analysis. The forecast was performed from 2021 to 2030. 11 statistical parameters were considered for performance evaluation. Additionally, four evaluation plots, comprising a quantile–quantile plot (QQ plot), a residual plot, a Bland Altman plot, and Theil’s U statistic, were employed for a detailed understanding of model accuracy. Results demonstrate that the random forest regression technique exhibited superior accuracy compared to SVM and GBM in training and testing stages. Notably, the coefficient of determination reached 97 % during the testing phase, emphasizing the robustness of this model. While Mean Absolute Error is lower (1085.071 cubic meter per second), in training, the model captures relative changes (Mean Absolute Percentage Error = 0.154) better during prediction. Willmott’s Index in training (0.77) and testing (0.55) suggest the model memorizes training data well and outperforms the other models in testing stage. The findings underscore the efficacy of RF regression as a superior alternative for short-term discharge forecasting, offering valuable insights for integrated water resources management, particularly in flood warning systems and the expansion of irrigation initiatives.

Abstract Image

从数据到决策:利用 ML 改进孟加拉国的河流泄量预报
河流排水量预报处于环境管理的最前沿,通过其对防洪、水资源管理、生态保护和能源生产的影响,为可持续发展做出了重大贡献。本研究采用随机森林(RF)、支持向量机(SVM)和梯度提升机(GBM)技术,对孟加拉国 Nilphamari 地区的年度河流排水量进行了预测。从八个地表水站获得的 1990 年至 2020 年的历史河流排水量数据是分析的基础。预测时间为 2021 年至 2030 年。性能评估考虑了 11 个统计参数。此外,为了详细了解模型的准确性,还采用了四种评价图,包括量化-量化图(QQ 图)、残差图、Bland Altman 图和 Theil's U 统计量。结果表明,在训练和测试阶段,随机森林回归技术的准确性优于 SVM 和 GBM。值得注意的是,在测试阶段,确定系数达到了 97%,强调了该模型的稳健性。虽然平均绝对误差较低(1085.071 立方米/秒),但在训练阶段,该模型能更好地捕捉到预测过程中的相对变化(平均绝对百分比误差 = 0.154)。训练中的威尔莫特指数(0.77)和测试中的威尔莫特指数(0.55)表明,该模型能很好地记忆训练数据,并在测试阶段优于其他模型。研究结果表明,射频回归是短期排泄量预测的一种有效替代方法,为水资源综合管理,特别是洪水预警系统和扩大灌溉范围提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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