RESERVOIR INFLOW FORECASTING BASED ON GRADIENT BOOSTING REGRESSOR MODEL — A CASE STUDY OF BHADRA RESERVOIR, INDIA

M. Rajesh, P. Indranil, S. Rehana
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Abstract

Reservoirs are essential infrastructures in human life. It provides water supply, flood control, hydroelectric power supply, navigations, irrigation, recreation, and other functionalities. To provide these services and resources from the reservoir, it’s necessary to know the reservoir system's inflow. The Machine Learning (ML) techniques are widely acknowledged to forecast the inflow into the reservoir system. In this paper, the popular ML technique, Gradient Boosting Regressor (GBR), is used to predict the reservoir system's inflow. This technique has been applied to the Bhadra reservoir of India at a daily time scale. In this study, the effect and complex relationship of climate phenomenon indices with inflow has been considered. The considered climate phenomenon indices are (1) Arctic Oscillation (AO), (2) East Pacific/North Pacific Oscillation (EPO), (3) North Atlantic Oscillation (NAO), (4) Extreme Eastern Tropical Pacific SST (NINO1+2), (5) Eastern Tropical Pacific SST (NINO3), (6) Central Tropical Pacific SST (NINO4), (7) East Central Tropical pacific SST (NINO34), (8) Pacific North American Index (PNA), (9) Southern Oscillation Index (SOI), (10) Western Pacific Index (WP), (11) Seasonality. In this paper, different parameter settings have been discussed on the models’ performances. The analysis of the GBR method for the Bhadra reservoir includes the number of estimators, maximum depth. The results indicate that the GBR model can capture the inflow's peaks and droughts into the reservoir systems. The study demonstrates how ML methods can be used to generate accurate reservoir inflow predictions.
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基于梯度增强回归模型的水库入库预测——以印度巴德拉水库为例
水库是人类生活必不可少的基础设施。它提供供水、防洪、水力发电、导航、灌溉、娱乐和其他功能。为了从水库中提供这些服务和资源,有必要了解水库系统的流入情况。机器学习(ML)技术在预测油藏系统的流入方面得到了广泛的认可。本文采用流行的ML技术梯度增强回归器(GBR)来预测水库系统的入流。该技术已应用于印度巴德拉水库的每日时间尺度。本研究考虑了气候现象指数对入流的影响及其复杂关系。考虑的气候现象指数有:(1)北极涛动(AO),(2)东太平洋/北太平洋涛动(EPO),(3)北大西洋涛动(NAO),(4)极端东热带太平洋海温(NINO1+2),(5)东热带太平洋海温(NINO3),(6)热带太平洋中部海温(NINO4),(7)东热带太平洋海温(NINO34),(8)太平洋北美指数(PNA),(9)南方涛动指数(SOI),(10)西太平洋指数(WP),(11)季节性。本文讨论了不同参数设置对模型性能的影响。对巴德拉水库的GBR方法进行了分析,包括估计器数、最大深度等。结果表明,GBR模型能较好地反映水库系统的入库高峰期和干旱期。该研究展示了如何使用ML方法来生成准确的油藏流入预测。
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