使用启发式机器学习方法进行流量预测

R. Adnan, Zhongmin Liang, Alban Kuriqi, O. Kisi, Anurag Malik, Binquan Li
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引用次数: 2

摘要

水流预报对水资源系统的设计和管理至关重要。利用巴基斯坦Neelum河和Kunhar河的月度流量数据,对人工神经网络遗传算法(ANN-GA)和自适应神经模糊推理系统遗传算法(anfiss - ga)两种启发式方法在流量预测中的精度进行了评价。采用统计指标对两种方法在不同时滞输入组合下的预测能力进行检验,并与M5回归树(M5RT)模型进行比较。结果表明,ANN-GA和ANFIS-GA的预测精度优于M5RT模型。月数的增加对模型的预测精度有积极的周期性影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Streamflow forecasting using heuristic machine learning methods
Streamflow forecasting is vital for designing and managing water resources systems. This study evaluates the prediction accuracy of two heuristic methods, artificial neural network-genetic algorithm (ANN-GA) and adaptive neurofuzzy inference system-genetic algorithm (ANFIS-GA) in streamflow prediction using monthly streamflow data of Neelum and Kunhar Rivers of Pakistan. The prediction capability of two methods are tested using the different time lags input combinations using statistical indicators and compared with M5 Regression Tree (M5RT) model. In results, it is found that ANN-GA and ANFIS-GA provided better prediction accuracy than M5RT model. Addition of month number showed a positive effect of periodicity on the prediction accuracy of models.
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