多输入单输出(MISO)洪水预报系统ARX与NARX模型的比较研究

F. Ruslan, A. Samad, Z. Zain, R. Adnan
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引用次数: 7

摘要

洪水是一种最危险的自然灾害,对人类生命财产造成巨大威胁。因此,在进行可靠的洪水水位预测之前,准确的洪水水位预测是非常重要的。尽管非线性模型在洪水水位预测中得到了广泛的应用,但线性模型在世界范围内仍是一种新兴的模型。因此,本文提出了MISO线性系统辨识模型,即自回归外生输入(ARX)。本研究中处理的河流分支是位于八打岭大桥的巴生河,它起源于三条上游河流,分别是苏莱曼大桥的巴生河,屯霹雳大桥的巴生河和加兰议会门的贡巴克河。所得的结果不太令人满意,还有改进的余地。这是由于洪水水位本身的动力学特性是高度非线性的。在此基础上,提出了基于非线性模型的洪水水位预测方法——非线性自回归模型(NARX)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiple Input Single Output (MISO) ARX and NARX model of flood prediction system: A comparative study
Flood is a most dangerous natural disaster that can cause enormous threat to human life and property. Thus, an accurate flood water level prediction is very prominent prior to develop a reliable flood water level prediction. Despite the widespread use of nonlinear model for flood water level prediction, linear model is still a new model among researchers around the world. Therefore, this paper present MISO linear system identification model namely Autoregressive Exogenous Input (ARX). The river branch treated in this study was Kelang river, located at Petaling bridge that originated from three upstream rivers which were Kelang river at Sulaiman bridge, Kelang river at Tun Perak bridge and Gombak river at Jalan Parlimen. The result obtained was not promising enough and there still rooms for improvement. This is due to the dynamics of flood water level itself is characterized as highly nonlinear. Thus, flood water level prediction using nonlinear model, Nonlinear Autoregressive Model with Exogenous Input (NARX) was proposed and results showed significant improvement.
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