Algae bloom prediction method based on principal component analysis and BP neural network
A technology of BP neural network and principal component analysis, applied in neural learning methods, biological neural network models, prediction, etc., can solve problems such as low prediction accuracy, redundant samples and large randomness
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Embodiment 1
[0068] The invention provides a method for predicting algal blooms based on a BP neural network. like figure 1 The flow shown, the specific steps are as follows:
[0069] Step 1. Monitoring data preprocessing;
[0070] 1. Data outlier processing;
[0071] The data used are 13 monitoring index values of PH, ammonia nitrogen, conductivity, water temperature, dissolved oxygen, chlorophyll, freshwater blue-green algae, redox potential, air temperature, air pressure, relative humidity, rainfall and light intensity during the monitoring period of a reservoir research water area , the frequency of data capture is once every 10 minutes. Since the monitoring data is susceptible to external interference and the data is random, there will be outliers in the monitoring data that are obviously inconsistent with the actual situation. The present invention adopts the Chauvier criterion to determine the abnormal value in the sample data, that is, in a certain monitoring data, n times of...
Embodiment 2
[0135] A method for predicting algal blooms based on BP neural network, said method comprising the following steps:
[0136] Step 1. Monitoring data preprocessing;
[0137] The data used are pH, ammonia nitrogen, chemical oxygen demand, water temperature, dissolved oxygen, chlorophyll, air pressure, sea level air pressure, maximum air pressure, minimum air pressure, maximum wind speed, maximum wind speed, average wind speed, temperature / air temperature, maximum air temperature, 21 monitoring index values such as minimum temperature, relative humidity, water vapor pressure, minimum relative humidity, rainfall and light intensity. Similar to Embodiment 1, data outlier processing, data smoothing processing, and data type conversion are sequentially performed here.
[0138] Step 2. Influencing factor analysis based on principal component analysis;
[0139] 1. Calculate the correlation matrix of each indicator
[0140] The correlation analysis of the 21 monitoring indicators i...
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