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27331 |
Spatial modeling of land subsidence using machine learning models and statistical methods
Enthalten in Environmental science and pollution research Bd. 29, 6.1.2022, Nr. 19, date:4.2022: 28866-28883
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27332 |
Spatial modeling of soil chemical properties in an arid region of Central Iran using machine learning and remote sensing data
Enthalten in Modeling earth systems and environment Bd. 11, 1.3.2025, Nr. 2, date:4.2025: 1-19
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27333 |
Spatial modeling of flood susceptibility using machine learning algorithms
Enthalten in Arabian journal of geosciences Bd. 14, 30.10.2021, Nr. 21, date:11.2021: 1-18
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27334 |
Spatial modulation and recurrent neural network based equalizer for MIMO communication systems
Enthalten in International journal of information technology Bd. 16, 27.7.2024, Nr. 7, date:10.2024: 4573-4587
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27335 |
Spatial omics-based machine learning algorithms for the early detection of hepatocellular carcinoma
Enthalten in Communications medicine Bd. 4, 3.12.2024, Nr. 1, date:12.2024: 1-11
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27336 |
Spatial prediction of flood susceptible areas using machine learning methods in the Siahkhor Watershed of Kermanshah province
Enthalten in Earth science informatics Bd. 18, 10.12.2024, Nr. 1, date:1.2025: 1-13
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27337 |
Spatial prediction of forest fires in India: a machine learning approach for improved risk assessment and early warning systems
Enthalten in Environmental science and pollution research Bd. 32, 1.2.2025, Nr. 8, date:2.2025: 4856-4878
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27338 |
Spatial prediction of groundwater levels using machine learning and geostatistical models: a case study of coastal faulted aquifer systems in southeastern Tunisia
Enthalten in Hydrogeology journal Bd. 31, 22.8.2023, Nr. 6, date:9.2023: 1387-1404
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27339 |
SPATIAL PREDICTION OF RECEIVED SIGNAL STRENGTH FOR CELLULAR COMMUNICATION USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOURS REGRESSION
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-4/W9-2024, 2024: 291-297. 7 S.
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27340 |
Spatial prediction of soil contamination based on machine learning: a review
Enthalten in Frontiers of environmental science & engineering Bd. 17, 17.2.2023, Nr. 8, date:8.2023: 1-17
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