IoT Water Accumulation Risk Prediction via Modular Segmentation
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Solution Overview
Problem
There is a need for a method to predict water accumulation risks in smart cities, enabling timely countermeasures to prevent casualties and economic losses.
Innovation Solution
A method implemented in an IoT system that predicts water accumulation risks by analyzing area information, determining adjustment schemes, and executing instructions to mitigate risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If water accumulation prediction and response system is implemented, then ability to prevent casualties and economic losses is improved, but system complexity and implementation cost increase
Solution Approach 1:
The system divides the smart city into multiple monitoring zones with distributed sensors, and segments the prediction model into modular components that process different types of data (precipitation, terrain, drainage capacity, historical data) independently before integration. This modular architecture reduces overall system complexity while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The system performs preliminary risk assessment and prediction before actual water accumulation occurs by analyzing historical data and current conditions. Adjustment schemes are pre-calculated and prepared in advance, enabling rapid response when thresholds are exceeded, thus preventing casualties and economic losses without requiring complex real-time decision-making systems.
2Measurement precision
If comprehensive area information is collected and analyzed, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
Historical water accumulation data, terrain information, and drainage capacity data are pre-processed and stored in optimized formats before needed. Prediction models are pre-trained and validated, allowing the system to quickly generate accurate predictions when new precipitation data arrives, reducing real-time processing time while maintaining high accuracy.
Solution Approach 2:
The system dynamically adjusts the level of detail and granularity of data processing based on current conditions. During low-risk periods, simplified models with fewer parameters are used. When precipitation thresholds are approached or exceeded, the system automatically activates more comprehensive models with additional parameters, balancing accuracy requirements with processing time constraints.
Data Source
AI summary
The embodiments of the present disclosure provide a method for predicting a water accumulation risk in a smart city implemented based on a management platform of an Internet of Things (IoT) system for predicting a water accumulation in a smart city. The method may include: predicting, based on obtained area information of a target area, a water accumulation risk in the target area; determining, based on the water accumulation risk, an adjustment scheme corresponding to the target area; and executing an adjustment instruction corresponding to the adjustment scheme.