Distributed Flood Modeling for Impervious-Surface Sensor Placement
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Solution Overview
Problem
Conventional flood prediction models fail to accurately predict flooding in regions without water bodies and do not account for the impact of impervious surfaces, leading to ineffective placement of flood sensors.
Innovation Solution
A computer-implemented method using distributed parameter flood modeling to simulate flooding, identify geographical clusters, and determine optimal sensor locations based on normalized inundation scores and cluster coverage, incorporating rainfall intensity data and topographical features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional flood prediction models are used that rely on bodies of water, then the models can accurately predict flooding in regions with rivers or lakes, but the models fail to provide accurate predictions in regions without water bodies (e.g., flash flooding in desert regions)
Solution Approach 1:
The model transitions from relying on the presence parameter of water bodies to using rainfall intensity and impervious surface parameters. By changing the input parameters from hydrological features (rivers, lakes) to meteorological and surface characteristics (rainfall intensity, impervious surface percentage), the model can accurately predict flooding in both traditional water-body regions and arid regions without permanent water bodies.
2Ease of manufacture
If conventional models do not account for impervious surfaces, then the models are simpler to implement, but the models underestimate water flow effects in urban regions with high impervious surface coverage
Solution Approach 1:
The model incorporates impervious surface percentage as a spatially varying parameter that affects water flow locally. Different regions are assigned different impervious surface characteristics (e.g., urban areas with high imperviousness versus rural areas with low imperviousness), allowing the model to accurately capture local water flow dynamics in urban environments while maintaining overall model simplicity through standardized parameter integration.
3Quantity of substance
If the number of flood sensors placed is limited, then the monitoring cost is reduced, but the coverage of flooding-related objectives is insufficient
Solution Approach 1:
The distributed parameter flood model performs preliminary simulation and identification of high-risk geographical clusters before sensor deployment. By pre-identifying critical areas through rainfall intensity and impervious surface analysis, the system determines optimal sensor locations in advance, ensuring that limited sensors are placed where they will provide maximum monitoring coverage and reliability.
Solution Approach 2:
The optimization process uses normalized inundation scores derived from model parameters to rank potential sensor locations. By transforming continuous model outputs into discrete location recommendations, the system converts the complex flood risk assessment into actionable sensor placement guidance, maximizing monitoring effectiveness with limited sensor resources.
Data Source
AI summary
A computer-implemented method includes executing, via a flood simulation model, a computer simulation that simulates flooding within a target spatial area based on an input of a geospatial dataset and the rainfall intensity data of one or more flooding events associated with the target spatial area; determining normalized inundation scores for a set of geographical cells and one or more clusters of interconnected geographical cells based on executing the computer simulation; determining, via an optimization algorithm, a set of optimal sensor locations in the target spatial area based on the normalized inundation scores and the one or more clusters of interconnected geographical cells; and generating a sensor location map that includes the set of geographical cells and a set of geospatial markers identifying the set of sensor locations; and generating a visual output that displays the sensor location map.