Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction Mónica Denham, Ana Cortés, Tomàs Margalef and Emilio Luque [email protected], {ana.cortes,tomas.margalef,emilio.luque}@uab.es Computer Architecture & Operating Systems Department
26
Embed
Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction
Mónica Denham, Ana Cortés, Tomàs Margalef and Emilio Luque
Dynamic Data Driven Genetic Algorithm. Analytical Method.
Experiments and Results.
Conclusions.
Experiments & Results (I)
• DDD Genetic Algorithm reduces error for both Calibration and Prediction stages.
• Prediction stage shows bigger errors (this stage works with calibration best individual for previous time step).
Experiments & Results (II)
• DDD Genetic Algorithm reduces error for both Calibration and Prediction stages.
Experiments & Results (III)
• Real map: fire behavior is different through time steps.• Errors are bigger than previous maps (synthetic maps).• Errors are similar for different configurations of our method.
Content Problem.
Method description.
Dynamic Data Driven Genetic Algorithm. Analytical Method.
Experimentation and Results.
Conclusions.
Conclusions
Calibration and Prediction stages have shown expected behavior.
We have used these techniques for real and synthetic burnings. Proposed methods shown good performance.
Using DDD Genetic Algorithm we could improve whole prediction process quality for synthetic cases.
Although real fires are our main objective, synthetic cases are the first step for understanding and improving steering methods.
Real fires characteristics are more difficult to simulate. Simulators implement abstractions of reality. We are working in this topic now.
Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction
double cros =rand() % 1000; if (cros <= crosp) //si se supera la probabilidad se cruzan { crospoint = (int) rand()% og1.n; for(int i=0;i<crospoint;i++) { ng1->p[i] = og1.p[i]; ng2->p[i] =(og2.p[i]+og1.p[i])/2; } for(int i=crospoint;i<og1.n;i++) { ng1->p[i] =(og2.p[i]+og1.p[i])/2; ng2->p[i] =og2.p[i]; } } else // se copian directamente los padres a los hijos { indcpy(ng1,og1); indcpy(ng2,og2); }}Volver
Viento Sigma = acumulación de la contribución a la intensidad de reacción
(modelo comb.) Beta = acumulación carga/densidad de todas las partículas del modelo. betaOpt = 3.348 / sigma0.8189
Ratio = beta/betaOpt c = 7.47 * exp(-0.133 * sigma0.55)) e = 0.715 * exp(-0.000359 * sigma) WindK = c * ratio-e
WindB = 0.02526 * sigma0.54
Fuel_PhiWind = WindK * windSpeedWindB
Rw = Fuel_Spread0() * Fuel_PhiWind
Depende del individuoDepende del modelo de combustibleVolver
Pendiente
Volver
Beta = acumulación carga/densidad de todas las partículas del modelo.
SlopeK = 5.275 * beta-0.3
Fuel_PhiSlope = SlopeK * Slope2
Rs = Fuel_Spread0() * Fuel_PhiSlope
Depende del individuoDepende del modelo de combustible
Algoritmo Genético
Algoritmo inspirado en la selección natural y en la genética. - Trabaja sobre una población de individuos.- De forma iterativa, se evoluciona la población mediante las operaciones de:
- Selección: competición de los individuos candidatos: los mejores individuos tienen mayor probabilidad de generar nuevos individuos. Función de evaluación. Elitismo.- Crossover: bajo una probabilidad se elije un crosspoint y los hijos reciben una parte de cada padre.- Mutación: bajo una probabilidad se muta el valor de un “cromosoma”.