LSTM-CNN Hybrid Network for Wildfire Risk Prediction
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Solution Overview
Problem
Current technologies lack an effective method for predicting wildfires based on biophysical indicators and spatiotemporal properties, which is crucial for assessing risk and planning disaster mitigation strategies.
Innovation Solution
A system utilizing a Long Short-Term Memory (LSTM) network combined with a Convolutional Neural Network (CNN) processes biophysical data from satellite images to predict wildfire risk by incorporating spatial and temporal properties of geographical areas, with the LSTM network using multiple units associated with historical time periods and CNNs trained with ground truth data to generate accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods are used, then the system is simple, but the prediction accuracy and reliability are insufficient
Solution Approach 1:
The patent combines CNN and LSTM networks into a hybrid architecture where CNN extracts spatial features from biophysical indicators while LSTM captures temporal dependencies in time series data. This merging of two different neural network types enables simultaneous processing of spatial and temporal properties, achieving high prediction accuracy for wildfire risk without sacrificing system manageability through modular design
Solution Approach 2:
The prediction system is segmented into distinct functional components: CNN modules for spatial feature extraction from each biophysical indicator, LSTM units for temporal pattern recognition across historical time periods, and separate processing streams for each indicator type. This segmentation allows complex processing to be broken down into manageable, specialized sub-tasks that can be optimized independently
2Reliability
If multiple biophysical indicators are processed, then the prediction reliability improves, but the computational complexity increases
Solution Approach 1:
The neural network architecture is designed with universal processing components that handle multiple biophysical indicators through the same CNN-LSTM framework. Each indicator type (NDVI, EVI, LAI, FAPAR, GPP, NPP) is processed by identical structural elements, allowing the system to scale to multiple indicators without proportionally increasing overall system complexity. The shared architecture provides efficiency while maintaining comprehensive analysis
Solution Approach 2:
The patent transforms multi-indicator data into a unified spatiotemporal representation where multiple biophysical indicators are processed as parallel channels through the CNN-LSTM network. This dimensional approach allows simultaneous processing of multiple indicators by treating them as additional feature dimensions rather than separate processing streams, reducing computational overhead while maintaining comprehensive analysis
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for predicting wildfires on the basis of biophysical indicators and spatiotemporal properties. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a long short term memory (LSTM) network. The LSTM network includes a convolutional neural network (CNN) for each of multiple LSTM units. Each LSTM unit and each CNN are associated with a historical time period in a time series. The LSTM is used to generate at least one prediction for wildfire risk for the at least one geographical area for an upcoming time period. The at least one prediction is provided responsive to the request.