Method for predicting chlorophyll a concentration in water based on BP nerval net

A BP neural network and concentration prediction technology, applied in the direction of testing water, general water supply saving, instruments, etc., to achieve the effect of improving prediction quality, reducing materials and reducing quantity

Active Publication Date: 2008-04-09
TIANJIN MUNICIPAL ENG DESIGN & RES INST
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a method for predicting chlorophyll a concentration in water bodie

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  • Method for predicting chlorophyll a concentration in water based on BP nerval net
  • Method for predicting chlorophyll a concentration in water based on BP nerval net
  • Method for predicting chlorophyll a concentration in water based on BP nerval net

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Embodiment Construction

[0018] The method for predicting the concentration of chlorophyll a in water based on the BP neural network of the present invention will be described in detail below in conjunction with the embodiments.

[0019] Chlorophyll a concentration prediction method in water body based on BP neural network of the present invention, comprises the steps:

[0020] (1) Obtain the value of chlorophyll a in the measured water body and other related water quality indicators that affect it as the detection data.

[0021] Obtaining other water qualities that have an impact on chlorophyll a in the embodiments of the present invention is to take a water sample every three days at 9 monitoring points of Jinhe and Weijinhe, monitor continuously for 14 times, and measure ammonia nitrogen (NH 3 -N), total nitrogen (TN), total phosphorus (TP), orthophosphate (DRP), permanganate index (COD Mn ), temperature (T), dissolved oxygen (DO), pH, suspended solids (SS), five-day biochemical oxygen demand (BOD...

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Abstract

The invention relates to a density forecast method of the chlorophyll a stemmed from a water body of the BP neural network. The density forecast method comprises the following steps: (1) the chlorophyll a in the tested water body and the value of other correlative water quality index which influences the chlorophyll a are acquired as the examination data. (2) The neural network of an error back propagation is established. (3) The neural network is trained and tested. (4) The neural network which passes the test is utilized to forecast the chlorophyll a in the water body. Other water qualities which influence the chlorophyll a are: Ammonia nitrogen, total nitrogen, total phosphorus, orthophosphate, permanganate index, temperature, dissolved oxygen, pH, suspension, five-day biochemical oxygen demand. The step (1) also comprises a normalization process. The data of the chlorophyll a and other ten water quality indexes are between -1 and +1 after the data of the chlorophyll a and other ten water quality indexes are normalized. The neural network comprises an input layer, an intermediate layer and an output layer. The invention can establish a forecast model related to the chlorophyll a, just needing the experiment which has the limited times. The chlorophyll in the river can be accurately and quickly forecasted through the computer simulation experiment and the science forecast.

Description

technical field [0001] The invention relates to a method for predicting chlorophyll in rivers, in particular to a method for predicting chlorophyll a concentration in water bodies based on BP neural network which can accurately and quickly predict chlorophyll in rivers. Background technique [0002] There are complex physical, chemical and biological processes in water bodies, and it is very difficult to predict the change of chlorophyll-a concentration after the nutrient level increases. Although traditional forecasting methods (including deterministic models and empirical models) can predict the concentration of chlorophyll a, they are difficult to be directly applied due to the long data correction process, especially the determination of some parameters. In addition, traditional prediction models tend to ignore important factors that affect water eutrophication, such as ecological factors, and these factors will limit the complexity of the model. A neural network is a m...

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Application Information

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IPC IPC(8): G01N33/18G06F17/00
CPCY02A20/152
Inventor 赵乐军李贺刘春光王秀朵庄源益
Owner TIANJIN MUNICIPAL ENG DESIGN & RES INST
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