使用ARIMA机器学习和集成模型的两步每日油藏流入预测

IF 2.4 3区 环境科学与生态学 Q2 ENGINEERING, CIVIL Journal of Hydro-environment Research Pub Date : 2022-11-01 DOI:10.1016/j.jher.2022.10.002
Akshita Gupta, Arun Kumar
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引用次数: 0

摘要

水库在文明发展中发挥着至关重要的作用,因为它们有助于将水储存用于水力发电、防洪、灌溉和饮用水等多种用途。为了有效满足这些多种用途,了解水库的流入量至关重要。除了历史数据外,未来对资金流入的预测也是必要的,特别是在气候变化的背景下。提出了一种预测水库入库流量的两步算法,以便在考虑入库流量历史变化的情况下,对水库日常运行进行细致的规划和执行。所开发的算法使用时间序列分析考虑了历史流入数据中的模式,以及使用机器学习模型中的不同预测因子考虑了气候模式的可变性。第一步使用时间序列模型,ARIMA方法来预测每月流入量,然后在第二步中使用两种类型的集成模型,即机器学习中的平均模型和提升模型,将其作为每月每日流入量预测的目标。测试结果表明,与非季风期相比,对于月模型和日模型,季风期的NRMSE和NMAE值均较低。在最大月数方面,平均系综模型的性能优于提升系综模型。年度结果显示,所有测试用例的实际值和预测值之间的误差小于5%,表明了所开发算法的精度。此外,不确定性分析表明,使用不同流入情景的加权平均进行的预测比针对单一流入情景的预测表现更好。
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Two-step daily reservoir inflow prediction using ARIMA-machine learning and ensemble models

The reservoirs play a crucial role in the development of civilisation as they facilitate the storage of water for multiple purposes like hydroelectric power generation, flood control, irrigation, and drinking water etc. In order to effectively meet these multiple purposes, the knowledge of the inflow in the reservoir is essential. Apart from the historical data, future prediction of the inflows is also necessary specially in context of climate change. A two-step algorithm for the prediction of reservoir inflow to enable meticulous planning and execution of daily reservoir operation keeping the historical variation of inflow in account has been proposed. The developed algorithm takes into account the patterns in the historic inflow data using the time series analysis along with the variability in the climatic patterns using the different predictors in the machine learning model. The first step uses time series model, ARIMA method to forecast the monthly inflows, which are then used as the targets in the second step for the month-wise daily forecasting of the inflows using the two types of ensemble models, namely, averaging and boosting models in machine learning. The test results show that for both the monthly models and daily models the NRMSE and NMAE values were low for the monsoon periods compared to the non-monsoon periods. The averaging ensemble models were found to perform better than the boosting ensemble models for maximum number of months. The yearly results show an error of less than 5% between actual and predicted values for all the test cases, showing the precision in the developed algorithm. Further, the uncertainty analysis shows that the prediction done using the weighted average of the different inflow scenarios performs better than the prediction against the single inflow scenario.

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来源期刊
Journal of Hydro-environment Research
Journal of Hydro-environment Research ENGINEERING, CIVIL-ENVIRONMENTAL SCIENCES
CiteScore
5.80
自引率
0.00%
发文量
34
审稿时长
98 days
期刊介绍: The journal aims to provide an international platform for the dissemination of research and engineering applications related to water and hydraulic problems in the Asia-Pacific region. The journal provides a wide distribution at affordable subscription rate, as well as a rapid reviewing and publication time. The journal particularly encourages papers from young researchers. Papers that require extensive language editing, qualify for editorial assistance with American Journal Experts, a Language Editing Company that Elsevier recommends. Authors submitting to this journal are entitled to a 10% discount.
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