Back to results
US 2026/0115508 A1Published application
Wildfire Risk Mitigation With Constraint Optimization
AssigneeXylo Risk, Inc.
InventorsScott Farley, David Winnacker
Life of this patent
Estimating the term…
Abstract
Machine-learning-based methods and systems for vegetation treatment project design. The approach autonomously creates, evaluates, and optimizes wildfire risk mitigation activities to intelligently reduce wildfire risk to communities in a resource-constrained environment. The methods and/or systems are accompanied by a cloud-enabled user interface that provides decision support to vegetation management specialists charged with implementing wildfire risk reduction measures.
Claims23
A computer-implemented system for wildfire risk reduction planning based on constrained optimization of a user-specified objective function, comprising:
- an asset database, configured for storing geospatial locations of one or more community assets, including one or more structures, infrastructure elements, or other values at risk;
- a constraint input module, configured for receiving a user-specified maximum budget constraint, expressed in dollars or other terms that describe a capacity to change landscape characteristics;
- a landscape database, configured for storing spatially explicit descriptions of the characteristics of the landscape, including one or more of slope, land elevation, vegetation type, size, volume, arrangement, continuity, or other values relevant to fire behavior predictions;
- a user-specified objective function input module, for accepting user input representing fire arrival time or some other attribute that quantifies risk of wildfire exposure to the assets in the asset database, given the landscape characteristics in the landscape database;
- a mitigation database, configured for storing a library of candidate risk reduction activities, each such activity comprising a geographic footprint;
- an effect model specifying:
- an expected change in landscape characteristics as a result of implementing one or more mitigations as specified by the candidate risk reduction activities, and
- an implementation cost expressed in terms that depend on the constraint;
- a candidate treatment module, configured to generate updates to the landscape database as a result of implementing one or more risk reduction activities as specified in the mitigation database;
- an evaluation module, configured to accept an output from the candidate treatment module and produce an estimate of the objective function;
- a subset generator, configured to generate subsets of the risk reduction activities from the mitigation database such that a total implementation cost of the subset is less than or equal to the user-specified constraint;
- and
- a selection module configured to select a subset of risk reduction activities having a high objective within the user-specified constraint.
10 more elements
A computer-implemented method for wildfire risk mitigation planning, the method comprising:
- (a) receiving, by one or more processors, multi-source geospatial and temporal data for a geographic region, such data comprising one or more of weather fields, topography, vegetation data, fuel condition data, and asset exposure data;
- (b) receiving a user-specified objective function representing fire arrival time or some other attribute that quantifies risk of wildfire exposure to assets in the geographic region, given the geospatial data;
- (c) generating, from the geospatial and temporal data and the objective function, a time-indexed spatial mesh of the geographic region comprising a plurality of cells at a temporal resolution and identifying a set of candidate ignition locations for a selectable forecast horizon;
- (d) simulating, from the candidate ignition locations and over time, a plurality of fire spread pathways using a spread model to produce, for each pathway, an ordered sequence of cell transitions with associated propagation likelihoods and intensity estimates;
- (e) computing, for each fire spread pathway, an impact score based at least on the associated propagation likelihoods, the intensity estimates, and exposure of assets intersected by the pathway using one or more fire spread models;
- (f) filtering the plurality of fire spread pathways by removing
- (a) pathways having a propagation likelihood below a threshold,
- (b) dominated pathways based on a multi-criteria dominance test using probability and impact, or
- (c) duplicate pathways based on path-overlap similarity;
- (g) constructing, from the filtered fire spread pathways, a directed graph comprising nodes representing respective (cell, time) states and edges representing transitions between the states, each edge annotated with a propagation likelihood and an incremental loss value, the directed graph including source nodes representing the candidate ignition locations and sink nodes representing termination states;
- (h) defining a library of candidate treatment actions, each treatment action comprising a geographic footprint mapped to a subset of the nodes or edges of the directed graph, an effect model specifying at least a reduction in propagation likelihood and/or intensity on affected edges, and an implementation cost;
- (i) evaluating, for each candidate treatment action and at least one combination of candidate treatment actions, a risk-reduction value computed as a change in expected loss on the directed graph when applying the effect model to the subset of nodes or edges associated with the candidate treatment actions;
- and
- (j) selecting, from the candidate treatment actions, a treatment plan comprising a subset of the candidate treatment actions that maximizes a risk-reduction objective subject to one or more constraints comprising a budget constraint and at least one resource, geographic, or timing constraint.
11 more elements
Dependencies are inferred from the claim text.