Water quality prediction method based on combination of support vector machine and KNN

A technology of support vector machine and water quality prediction, which can be used in prediction, general water supply saving, computer parts and other directions, can solve the problem of low prediction accuracy, and achieve the effect of excellent prediction effect.

Pending Publication Date: 2019-12-20
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the shortcomings of the low prediction accuracy of the existing water quality prediction methods, the present invention proposes a water quality prediction method based on the combination of support vector machine and KNN with high prediction accuracy

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  • Water quality prediction method based on combination of support vector machine and KNN
  • Water quality prediction method based on combination of support vector machine and KNN
  • Water quality prediction method based on combination of support vector machine and KNN

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Embodiment Construction

[0039] Below in conjunction with accompanying drawing, the present invention will be further described,

[0040] refer to figure 1 , a water quality prediction method based on the combination of support vector machine and KNN, including the following steps:

[0041] 1) Collect water quality data with robotic bionic fish, and preprocess the data to establish a sample data set;

[0042] 2) The K-nearest neighbor algorithm is used to pre-classify the water quality data;

[0043] 3) Set up a support vector machine regression model, select the optimal parameters through the k-fold cross-validation algorithm, and construct the optimal support vector machine regression model;

[0044] 4) The optimal water quality prediction model is obtained through training, the temperature and pH value are predicted, and the prediction effect is evaluated.

[0045] Further, in said step 1), data preprocessing includes the following processes:

[0046] Step 101, converting the extracted data int...

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Abstract

The invention discloses a water quality prediction method based on SVM-KNN. The method comprises the following steps of 1) preprocessing the acquired water quality data; 2) pre-classifying the data byusing a K-nearest neighbor algorithm; 3) establishing a support vector machine regression model, selecting the optimal parameters of a water quality data prediction model through a k-fold cross validation algorithm, and applying the optimal parameters to the model; and 4) training the model, and evaluating the prediction effect by taking the temperature and the pH value as the prediction targets.According to the water quality prediction method combining the KNN and the support vector machine, a water quality time sequence is predicted, the prediction accuracy is improved, the method belongsto the technical field of water quality prediction methods, and the accurate prediction of the water quality is successfully achieved.

Description

technical field [0001] The invention relates to the technical field of environmental pollution water quality monitoring, in particular to a water quality prediction method based on the combination of support vector machine (SVM) and KNN. Background technique [0002] Ecology prospers, civilization prospers. In recent years, with the deepening of industrialization, the discharge of industrial, agricultural and domestic sewage has seriously damaged the environment. The problem of water pollution has been closely related to the life and health of the people. Water quality prediction is an important method of water resource management and water resource control. It provides an important scientific basis and technical support for timely grasping the development trend of water quality changes, water quality prediction and early warning, and water pollution control. [0003] With the rise of the Internet of Things technology and the advent of the era of big data, artificial intell...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06K9/62G06Q50/26
CPCG06Q10/04G06Q50/26G06F18/24147G06F18/2411G06F18/214Y02A20/152
Inventor 洪榛李涛涛潘晓曼刘燕娜
Owner ZHEJIANG UNIV OF TECH
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