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Flood prediction method based on extreme learning machine

A technology of extreme learning machine and prediction method, which is applied in the direction of neural learning method, prediction, biological neural network model, etc., and can solve problems such as only considering rainfall, low efficiency, and inaccurate prediction results

Pending Publication Date: 2020-07-24
NORTH CHINA UNIV OF WATER RESOURCES & ELECTRIC POWER
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Problems solved by technology

[0004] However, the former method only considers the rainfall and does not take other factors into account, resulting in inaccurate prediction results, and the latter method uses artificial neural networks, which has the problem of low efficiency

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  • Flood prediction method based on extreme learning machine
  • Flood prediction method based on extreme learning machine
  • Flood prediction method based on extreme learning machine

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Embodiment Construction

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0025] The present invention provides a flood prediction method based on an extreme learning machine using an extreme learning machine, and the topology of the extreme learning machine is as follows figure 1 Shown, it comprises input layer, hidden layer and output layer, and described flood prediction method based on extreme learning machine comprises the following steps:

[0026] Step 1: Build a multi-factor index system. The multi-factors used in this exampl...

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Abstract

The invention discloses a flood prediction method based on an extreme learning machine, and relates to the technical field of disaster prediction. According to the invention, a flood prediction modelis established according to various flood causes, an extreme learning machine and a geographic information system (GIS); the efficiency and precision advantages of the extreme learning machine relative to an artificial neural network are verified by determining a coefficient r, a Wilter index WI, a Nash efficiency index Ens, a root mean square error RMSE, a mean absolute error MAE and a related error RE. Experimental results show that the learning speed of the extreme learning machine is 32 times of that of an artificial neural network, the noise processing capacity of the extreme learning machine is superior to that of the artificial neural network, and compared with the artificial neural network, extreme learning has great advantages in prediction capacity and efficiency and is a more appropriate choice for flood forecasting models.

Description

technical field [0001] The invention relates to the technical field of disaster prediction, in particular to a flood prediction method based on an extreme learning machine. Background technique [0002] Flood is one of the most destructive natural disasters, and the occurrence of flood has caused great damage to the lives and properties of residents. Therefore, it is very urgent to establish a flood model to achieve early warning of regional floods. [0003] In recent years, due to the development of information science, machine learning algorithms such as artificial neural networks have been gradually applied to various fields. After reviewing relevant literature, it is found that the current flood prediction methods can be basically divided into two categories. One is to calculate the correlation between regional rainfall and runoff based on a linear model and then analyze the runoff to achieve the effect of flood prediction; the other is Based on the neural network, the...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/08
CPCG06Q10/04G06N3/08Y02A10/40
Inventor 刘扬刘雪梅王立虎闫新庆杨礼波刘明堂
Owner NORTH CHINA UNIV OF WATER RESOURCES & ELECTRIC POWER
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