Geospatial Risk Mapping With Hybrid DDM-AHP Vulnerability Assessment
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Solution Overview
Problem
Existing vulnerability assessment techniques for natural hazards, such as earthquakes and floods, are complex, time-consuming, and lack the ability to accurately assess physical risks due to slope instability, while statistical models fail to capture physical processes, and existing methods fail to address the quantitative relation between influencing factors and effects of natural disasters.
Innovation Solution
A method combining a data-driven model (DDM) with a neural network and an Analytical Hierarchy Process (AHP) model to assess physical risks, using multispectral and hyperspectral data, historical hazards, and influencing factors, with dynamic weight updates to improve accuracy and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based models are used for vulnerability assessment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines data-driven models (machine learning algorithms) with physics-based models to create a hybrid vulnerability assessment system. This integration allows the system to leverage the pattern recognition capabilities of data-driven approaches while incorporating the physical process understanding of physics-based models, thereby maintaining measurement precision while managing complexity through complementary strengths of different modeling paradigms
Solution Approach 2:
The patent introduces statistical models as intermediary components that bridge the gap between complex physics-based models and simpler data-driven models. These statistical models capture essential physical processes while being computationally more tractable, serving as a mediator that provides sufficient accuracy for vulnerability assessment without the full complexity of first-principle physics models
2Ease of operation
If expert driven AHP model is used for vulnerability assessment, then ease of operation is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces the manual, expert-driven AHP model with automated machine learning algorithms that can process geospatial data and perform vulnerability assessments without requiring expert intervention. This substitution maintains the simplicity of operation through automated workflows while dramatically improving productivity by eliminating time-consuming field investigations and manual scoring processes
Solution Approach 2:
The patent implements self-service capabilities where the system automatically performs vulnerability assessments using trained machine learning models that process geospatial data and generate results without requiring expert analysts. The system serves itself by automatically updating models with new data and generating assessments independently, thereby maintaining ease of operation while significantly increasing assessment throughput and productivity
3Device complexity
If statistical models are used for vulnerability assessment, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent uses statistical models as intermediary components that capture essential physical processes while remaining computationally tractable. These statistical models serve as a bridge between simple data-driven approaches and complex physics-based models, providing sufficient measurement precision for vulnerability assessment without requiring the full complexity of first-principle models
Solution Approach 2:
The patent combines statistical models with machine learning algorithms to create a hybrid approach that maintains measurement precision while managing complexity. The statistical models capture physical process understanding while the machine learning components handle pattern recognition and prediction, together achieving accurate physical risk assessment without the full complexity of pure physics-based models
4Measurement precision
If field investigation is performed for vulnerability score generation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent replaces time-consuming field investigations with remote sensing technologies and geospatial data analysis using machine learning models. The system processes satellite imagery, aerial photographs, and other remote sensing data to assess vulnerability without requiring physical site visits, thereby maintaining measurement precision through advanced image analysis while eliminating the time loss associated with field investigations
Solution Approach 2:
The patent uses remote sensing to create digital copies and representations of the physical environment through satellite imagery and aerial photography. These digital copies serve as proxies for physical field investigations, allowing the machine learning models to analyze vulnerability factors remotely with high precision while avoiding the time consumption of actual field visits and manual inspections
Data Source
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AI summary
This disclosure relates generally to system and method to assess physical risks from geospatial data of geographical region. Geospatial data analysis requires combination of data from multiple sources to assess specific vulnerability assessment challenges. The disclosed method maps various areas which are likely to be more or less susceptible to a particular hazard. The method of the present disclosure is a combinatorial approach of data driven model and analytical hierarchical process model which enables to assess vulnerability occurring in geographical region of interest. Here, a combined class vector for each pixel is determined using the first class vector and the second class vector based on one or more dynamic weights to train off the shelf model (OTSM). Finally, the trained OTSM physical risks and a physical risk map for a set of input images associated with the geographical region.