Watershed Pollution Risk Prediction via Segmented Fuzzy Models
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Solution Overview
Problem
Current water environment risk prediction and early warning methods are not generalizable or universal for entire river basins, leading to low efficiency in predicting and managing pollution risks across multiple sources.
Innovation Solution
A method that predicts discharge information from all pollution sources in a watershed, classifies risks using deep learning models and remote sensing technologies, and integrates fuzzy comprehensive models for risk assessment and warning, covering all sources and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If water environment risk prediction is performed for each pollution source individually, then prediction accuracy for single sources is improved, but the coverage and efficiency for entire river basin prediction deteriorates
Solution Approach 1:
The patent divides the river basin into multiple sub-regions and segments pollution sources into different categories based on their characteristics. This allows the system to handle individual pollution sources with specific prediction models while simultaneously covering the entire basin through organized segmentation, thus improving both accuracy and efficiency.
Solution Approach 2:
The patent develops a universal risk prediction framework that can handle multiple types of pollution sources (industrial, agricultural, domestic) and multiple pollutants simultaneously. The system uses a standardized assessment methodology that applies across the entire river basin, enabling efficient large-scale prediction without sacrificing adaptability to specific source characteristics.
2Adaptability or versatility
If comprehensive monitoring of all pollution sources in the river basin is implemented, then the coverage of pollution risk supervision is improved, but the complexity of the prediction system increases
Solution Approach 1:
The patent segments the comprehensive monitoring system into modular components: data collection modules for different pollution sources, processing modules for different pollutant types, and output modules for different risk levels. This modular segmentation enables the system to cover all pollution sources while managing complexity through organized, interchangeable modules.
Solution Approach 2:
The patent changes the parameter representation of pollution data by using standardized risk indices and classification categories. Instead of handling raw, diverse data from all sources, the system transforms data into uniform parameters (risk levels, pollution types, severity categories), which simplifies processing while maintaining comprehensive coverage of all pollution sources.
3Measurement precision
If detailed analysis of each pollution source is performed, then the accuracy of risk assessment is improved, but the time required for prediction and warning increases
Solution Approach 1:
The patent performs preliminary classification and screening of pollution sources before detailed risk assessment. Pollution sources are pre-categorized by type, location, and potential impact level, and historical data is pre-processed into standardized formats. This preliminary action reduces the complexity of subsequent detailed analysis, enabling accurate risk assessment without excessive time consumption.
Solution Approach 2:
The patent implements a multi-level assessment approach where low-risk pollution sources are quickly screened using simplified methods, and only high-risk sources undergo detailed analysis. This skipping approach allows the system to maintain high accuracy for critical sources while rapidly processing numerous lower-priority sources, significantly reducing overall prediction time.
Data Source
AI summary
A risk prediction and early warning method for water environments based on a water environment model predicting pollution discharge information for all pollution sources in a watershed, and including: selecting pollution sources requiring environmental risk prediction and early warning and dividing these into different risk prediction/early warning levels; determining from official environmental monitoring data and literature research initial elements for environmental pollution risk evaluation; obtaining principal pollution elements affecting pollution events; generating a plurality of environmental risk prediction and early warning models; forming a comprehensive fuzzy risk prediction and early warning model by combining the several risk prediction and early warning models having the best selective performance; inputting principal pollution element values into the comprehensive fuzzy risk prediction and early warning model, and predicting risk values for pollution events at pollution sources. The present method realizes prediction of watershed pollution risk, and resolves present deficiencies in watershed pollution risk prediction and early warning, while improving coverage rates for watershed pollution risk prediction and enhancing the accuracy thereof.