A novel modified bat algorithm to improve the spatial geothermal mapping using discrete geodata in Catalonia-Spain
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| izdano v: | Modeling Earth Systems and Environment vol. 10, no. 3 (Jun 2024), p. 4415 |
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| Drugi avtorji: | , , |
| Izdano: |
Springer Nature B.V.
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| Teme: | |
| Online dostop: | Citation/Abstract Full Text - PDF |
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Brez oznak, prvi označite!
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| 001 | 3064391008 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2363-6203 | ||
| 022 | |a 2363-6211 | ||
| 024 | 7 | |a 10.1007/s40808-024-01992-7 |2 doi | |
| 035 | |a 3064391008 | ||
| 045 | 2 | |b d20240601 |b d20240630 | |
| 100 | 1 | |a Mirfallah Lialestani, Seyed Poorya |u Universitat Politècnica de Catalunya, Department of Mining, Industrial and ICT Engineering, Manresa, Spain (GRID:grid.6835.8) (ISNI:0000 0004 1937 028X) | |
| 245 | 1 | |a A novel modified bat algorithm to improve the spatial geothermal mapping using discrete geodata in Catalonia-Spain | |
| 260 | |b Springer Nature B.V. |c Jun 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a Spain | |
| 651 | 4 | |a Catalonia Spain | |
| 653 | |a Mapping | ||
| 653 | |a Algorithms | ||
| 653 | |a Subsurface mapping | ||
| 653 | |a Deep learning | ||
| 653 | |a Adaptability | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Effectiveness | ||
| 653 | |a Power plants | ||
| 653 | |a Emissions | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Heat | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Geothermal power | ||
| 653 | |a Climate change | ||
| 653 | |a Industrial plant emissions | ||
| 653 | |a Efficiency | ||
| 653 | |a Greenhouse gases | ||
| 653 | |a Fossil fuels | ||
| 653 | |a Temperature | ||
| 653 | |a Renewable resources | ||
| 653 | |a Engineering | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Cost control | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Parcerisa, David |u Universitat Politècnica de Catalunya, Department of Mining, Industrial and ICT Engineering, Manresa, Spain (GRID:grid.6835.8) (ISNI:0000 0004 1937 028X) | |
| 700 | 1 | |a Himi, Mahjoub |u University of Barcelona, Department of Mineralogy, Petrology and Applied Geology, Barcelona, Spain (GRID:grid.5841.8) (ISNI:0000 0004 1937 0247) | |
| 700 | 1 | |a Abbaszadeh Shahri, Abbas |u Bircham International University, Department of Engineering and Technology, Madrid, Spain (GRID:grid.472233.3) (ISNI:0000 0004 0616 1884) | |
| 773 | 0 | |t Modeling Earth Systems and Environment |g vol. 10, no. 3 (Jun 2024), p. 4415 | |
| 786 | 0 | |d ProQuest |t Environmental Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3064391008/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3064391008/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |