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A novel modified bat algorithm to improve the spatial geothermal mapping using discrete geodata in Catalonia-Spain

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Publicado en:Modeling Earth Systems and Environment vol. 10, no. 3 (Jun 2024), p. 4415
Autor principal: Mirfallah Lialestani, Seyed Poorya
Otros Autores: Parcerisa, David, Himi, Mahjoub, Abbaszadeh Shahri, Abbas
Publicado:
Springer Nature B.V.
Materias:
Spain
Catalonia Spain
Mapping
Algorithms
Subsurface mapping
Deep learning
Adaptability
Heuristic methods
Effectiveness
Power plants
Emissions
Optimization techniques
Heat
Research & development > R&D
Geothermal power
Climate change
Industrial plant emissions
Efficiency
Greenhouse gases
Fossil fuels
Temperature
Renewable resources
Engineering
Alternative energy sources
Cost control
Environmental
Acceso en línea:Citation/Abstract
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Resumen:Metaheuristic algorithms due to flexibility can be applied to a wide range of complex engineering optimization problems. The effectiveness, efficiency, and adaptability of such algorithms can significantly be enhanced through the modified variants. In this paper a novel modified bat algorithm (MoBA) using the concept of expectation value is proposed and evaluated using different benchmark functions, and then compared and ranked among other previously improved variants. Subsequently, the proposed MoBA was hybridized with a pretrained multitask adaptive deep learning model to generate 3D spatial subsurface mapping of geothermal temperatures in Catalonia, Spain. The success, effectiveness and superiority of the presented MoBA in compare with previously modified firefly algorithm was confirmed using different accuracy performance criteria by at least 1.71% improvement.
ISSN:2363-6203
2363-6211
DOI:10.1007/s40808-024-01992-7
Fuente:Environmental Science Database