Groundwater Pollution Source Identification Using Neural Network Distance Metrics
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Solution Overview
Problem
Existing methods for identifying groundwater pollution sources, such as model algorithms and chemical tracing, face limitations due to nonlinear relationships in environmental data, leading to lower accuracy in pollution source identification.
Innovation Solution
A method utilizing a water pollution neural network that calculates Euclidean and clustering distances between sample data and output neuron weight vectors, performing weighted calculations to determine the winning neuron and updating the output neuron weights, ultimately determining the groundwater pollution source based on the updated network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear methods such as PCA and FA are used for processing groundwater data, then the processing is simple, but the accuracy of pollution source identification is low due to inability to handle nonlinear relationships
Solution Approach 1:
The patent transforms the groundwater pollution data from linear parameter space to a nonlinear feature space using self-organizing mapping neural networks. The method changes the parameters by introducing Euclidean distance and clustering distance as new feature dimensions, allowing the system to capture nonlinear relationships in the data while maintaining computational feasibility through standardized processing procedures
Solution Approach 2:
The patent replaces traditional linear mathematical methods (PCA, FA) with a neural network-based computational system. This substitution introduces a more complex computational mechanism that can handle nonlinear relationships, trading increased computational complexity for significantly improved identification accuracy in groundwater pollution source detection
2Reliability
If various groundwater indexes and large amounts of samples are collected, then the comprehensiveness of data is improved, but the difficulty of data processing increases
Solution Approach 1:
The patent segments the complex groundwater data processing task into distinct computational stages: data acquisition with multiple indexes, Euclidean distance calculation, clustering distance calculation, weighted feature extraction, and neural network classification. This segmentation allows comprehensive data to be processed systematically, reducing the overall difficulty by breaking down the complex processing into manageable steps
Solution Approach 2:
The patent introduces intermediate computational features (Euclidean distance and clustering distance) that serve as mediators between the raw comprehensive groundwater data and the final pollution source identification. These intermediate features transform the complex multi-index data into a standardized format that the neural network can process efficiently, reducing processing difficulty while maintaining data comprehensiveness
Data Source
AI summary
A groundwater pollution source identification method, comprising: acquiring sample data for groundwater pollution source detection, the sample data at least comprising water chemical index concentration data, pollutant concentration data, longitude and latitude coordinates, surface water system data, and enterprise type data; calculating the Euclidean distance and the clustering distance between the sample data and the corresponding output neuron weight vector; performing weighted calculation on the Euclidean distance and the clustering distance to determine the winning neuron; updating the output neuron weight vector of the water pollution neural network according to the input neuron weight vector and the output neuron weight vector of the winning neuron; and when the number of updating times of the output neuron weight vector of the water pollution neural network reaches a preset value, determining the groundwater pollution source of the target area by the updated water pollution neural network.