基于人工神经网络的洪水建模与预测

Awal Rais Sanubari, P. Kusuma, C. Setianingsih
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引用次数: 5

摘要

洪水是印尼常见的自然灾害类型之一,我们需要一个能够预测洪水到来的系统,这对印尼人民来说是很重要的,尤其是居住在河流流经某一地区的人们。一些可以用来预测洪水的参数是水位和河流周围的降雨量。洪水预测的建模系统必须具有尽可能准确的预测结果,才能产生一个好的洪水预测系统。因此,本研究提出了利用人工神经网络对洪水预测能力进行分析的方法,本研究案例采用了人工神经网络径向基函数。径向基函数是一种人工神经网络结构模型,由输入层、隐藏层和输出层三层组成。培训和测试过程使用的数据是2015年Dayeuhkolot的水位和降雨量数据。在训练和测试过程的预测结果中,隐藏节点= 35,学习率= 0.2,Spread常数= 1.1,目标历元最大终止为5000历元,水位数据的MAPE值分别为0.047%和1.05%,降雨数据的MAPE值分别为4.97%和29.1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flood Modelling and Prediction Using Artificial Neural Network
Flood is one of the common types of natural disaster in Indonesia, we need a system that can predict the arrival of the flood is important for the Indonesian people, especially people who live a certain area of the river flow. Some parameters that can be used to predict the flood are water level and rainfall around the river. Modeling system to predict the flood must have the prediction results as accurate as possible in order to produce a good system in predicting floods. Therefore, in this study proposed method of artificial neural network to analyze flood prediction ability by using artificial neural network In this study case using artificial neural network Radial Basis Function. Radial Basis Function is a model of artificial neural network architecture consisting of three layers of which are the input layer, hidden layer, and output layer. The data used for the training and testing process are data of water level and rainfall data in 2015 in Dayeuhkolot. Prediction results in the training and testing process resulted in MAPE values are 0.047% and 1.05% for water level data and 4.97% and 29.1% for rainfall data with combination of hidden node = 35, learning rate = 0.2 and Spread constant = 1.1 with the target epoch maximum termination of 5000 epoch.
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