AI Water Quality Forecasting via Sensor Data Integration
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Solution Overview
Problem
Current technologies lack a reliable and accurate method for forecasting water quality, as they face challenges in collecting and processing disparate data from various sources, including geographic distances between sensors and differences in data collected, which hinders the provision of meaningful and timely information for water quality forecasting.
Innovation Solution
A system utilizing AI algorithms that integrates hydrology data from public databases and real-time sensor data from IoT devices to generate a water quality index score, accounting for contaminants and their impact, and transmits this score to mobile devices for actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI algorithms are used to process water quality data, then forecasting accuracy is improved, but data integration complexity increases due to disparate sources and formats
Solution Approach 1:
The patent employs data normalization layers and standardized data schemas as intermediaries between diverse sensor sources and the AI processing system. These intermediaries translate disparate data formats, units, and protocols into a unified structure that the AI algorithm can process effectively, thereby maintaining high forecasting accuracy while managing integration complexity.
Solution Approach 2:
The system transforms raw sensor data into normalized parameters through calibration curves and conversion algorithms. By changing the parameter representation (e.g., converting different temperature scales, standardizing concentration units), the system enables accurate AI processing across multiple data sources without requiring complex custom integration logic for each source.
2Loss of information
If real-time sensor data from multiple IoT devices is integrated, then data comprehensiveness is improved, but processing time increases due to geographic distances and data differences
Solution Approach 1:
The patent divides the water quality monitoring system into geographically segmented zones, each with its own data processing pipeline. By segmenting the large-scale data integration problem into smaller regional units, the system can process data from multiple IoT devices more efficiently while maintaining comprehensive coverage across the entire monitoring area.
Solution Approach 2:
The system performs preliminary data validation, filtering, and preprocessing at the edge devices and local gateways before data transmission to central processing. This preliminary action reduces the volume and complexity of data requiring centralized processing, thereby decreasing overall processing time while preserving data comprehensiveness.
3Reliability
If contaminant-specific adjustments are made to AI output, then water quality index accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies contaminant-specific adjustment factors and weighting schemes tailored to local water quality conditions and regulatory requirements. By customizing the adjustment parameters for specific contaminants and geographic locations rather than using uniform adjustments, the system achieves higher water quality index accuracy while managing complexity through localized parameter sets.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable storage media for predicting the water quality within a geographic area based on hydrology data, contaminant data, and/or weather data using Artificial Intelligence (AI). The system can receive hydrology data for a predefined geographic region and real-time sensor data associated with water quality within the predefined geographic region. The system can then initiate a serverless AI algorithm using the hydrology data and the real-time sensor data, then receive output of the algorithm including an initial water quality score. The system can then adjust the initial water quality score based on contaminants within the predefined geographic region and transmit the resulting water quality index score to a mobile computing device.