ABSTRACT

This study evaluated the effectiveness of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite rainfall data for the development of multi-step ahead streamflow forecasting models. Daily time scale precipitation data of nearly three decades (1986–2012) over the Varahi river basin in Western Ghats of Karnataka, India were used for the analysis. Machine learning (ML) models, namely, the Group Method of Data Handling (GMDH), Chi-square Automatic Interaction Detector (CHAID), and Random Forest (RF) were simulated for one, three and seven days ahead streamflow forecasting. Additionally, the developed forecasting models were improved through the integration with Intrinsic Time-scale decomposition (ITD) (by decomposing the input data into a series of proper rotation components (PRC) and a monotonic trend). The uniqueness of this study lies in coupling ITD with machine learning models to forecast daily streamflow time-series. Concurrently, the precipitation data derived from India Meteorological Department (IMD) gridded rainfall dataset were also employed for developing analogous multistep ahead streamflow forecasting models. The proposed methodology was aimed to have an accurate and a reliable forecasting model that can assist water resources management and operation. Comparative performance evaluation using various statistical indices portrayed the superiority of CHIRPS satellite rainfall data product in forecasting daily streamflows up to a week lead time. The results indicate that, the hybrid ITD-based ML models developed using CHIRPS precipitation data as inputs held a better performance at all lead times.

摘要

本研究评估了气候危害组红外降水站 (CHIRPS) 卫星降雨数据对开发多步提前流量预测模型的有效性。分析使用了印度卡纳塔克邦西高止山脉 Varahi 河流域近三十年（1986-2012 年）的每日时间尺度降水数据。机器学习 (ML) 模型，即数据处理组方法 (GMDH)、卡方自动交互检测器 (CHAID) 和随机森林 (RF) 被模拟用于提前 1、3 和 7 天的流量预测。此外，通过与内在时间尺度分解 (ITD) 的集成（通过将输入数据分解为一系列适当的旋转分量 (PRC) 和单调趋势），改进了开发的预测模型。本研究的独特之处在于将 ITD 与机器学习模型相结合，以预测每日流量时间序列。同时，来自印度气象部门 (IMD) 网格降雨数据集的降水数据也被用于开发类似的多步前进流预测模型。所提议的方法旨在拥有一个准确可靠的预测模型，以帮助水资源管理和运营。使用各种统计指数的比较性能评估描绘了 CHIRPS 卫星降雨数据产品在预测长达一周的每日流量方面的优越性。结果表明，使用 CHIRPS 降水数据作为输入开发的基于混合 ITD 的 ML 模型在所有提前期都具有更好的性能。