Short-term River streamflow modeling using Ensemble-based additive learner approach

IF 2.4 3区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Khabat Khosravi , Shaghayegh Miraki , Patricia M. Saco , Raziyeh Farmani
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引用次数: 8

Abstract

Accurate streamflow (Qt) prediction can provide critical information for urban hydrological management strategies such as flood mitigation, long-term water resources management, land use planning and agricultural and irrigation operations. Since the mid-20th century, Artificial Intelligence (AI) models have been used in a wide range of engineering and scientific fields, and their application has increased in the last few years. In this study, the predictive capabilities of the reduced error pruning tree (REPT) model, used both as a standalone model and within five ensemble-approaches, were evaluated to predict streamflow in the Kurkursar basin in Iran. The ensemble-approaches combined the REPT model with the bootstrap aggregation (BA), random committee (RC), random subspace (RS), additive regression (AR) and disjoint aggregating (DA) (i.e. BA-REPT, RC-REPT, RS-REPT, AR-REPT and DA-REPT). The models were developed using 15 years of daily rainfall and streamflow data for the period 23 September 1997 to 22 September 2012. A set of eight different input scenarios was constructed using different combinations of the input variables to find the most effective scenario based on the linear correlation coefficient. A comprehensive suite of graphical (time-variation graph, scatter-plot, violin plot and Taylor diagram) and quantitative metrics (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliff efficiency (NSE), Percent of BIAS (PBIAS) and the ratio of RMSE to the standard deviation of observation (RSR)) was applied to evaluate the prediction accuracy of the six models developed. The outcomes indicated that all models performed well but the AR-REPT outperformed all the other models by rendering lower errors and higher precision across a number of statistical measures. The use of the BA, RC, RS, AR and DA models enhanced the performance of the standalone REPT model by about 26.82%, 18.91%, 7.69%, 28.99% and 28.05% respectively.

基于集成的加性学习方法的短期河流流建模
准确的流量预测可以为城市水文管理战略提供关键信息,如洪水缓解、长期水资源管理、土地利用规划以及农业和灌溉业务。自20世纪中期以来,人工智能(AI)模型已广泛应用于工程和科学领域,并且在过去几年中其应用有所增加。在这项研究中,减少误差修剪树(REPT)模型的预测能力,既可以作为一个独立模型,也可以作为五种集合方法,用于预测伊朗Kurkursar盆地的河流流量。集成方法将REPT模型与自举聚合(BA)、随机委员会(RC)、随机子空间(RS)、加性回归(AR)和分离聚合(DA)(即BA-REPT、RC-REPT、RS-REPT、AR-REPT和DA-REPT)相结合。这些模型是根据1997年9月23日至2012年9月22日15年的日降雨量和流量数据开发的。利用输入变量的不同组合,构建了一组8种不同的输入场景,根据线性相关系数找到最有效的场景。采用一套综合的图形(时变图、散点图、小提琴图和泰勒图)和定量指标(均方根误差(RMSE)、平均绝对误差(MAE)、纳什-萨特克里夫效率(NSE)、偏倚百分比(PBIAS)和RMSE与观测标准差(RSR)之比)来评估所建立的6种模型的预测准确性。结果表明,所有模型都表现良好,但AR-REPT通过在许多统计度量中呈现更低的误差和更高的精度而优于所有其他模型。使用BA、RC、RS、AR和DA模型后,独立的REPT模型的性能分别提高了约26.82%、18.91%、7.69%、28.99%和28.05%。
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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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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