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Land use land cover representation through supervised machine learning methods: sensitivity on simulation of urban thunderstorms in the east coast of India

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  • Kumari Priya

    (National Institute of Technology Rourkela)

  • Talukdar Sasanka

    (National Institute of Technology Rourkela)

  • Krishna K. Osuri

    (National Institute of Technology Rourkela)

Abstract

This study assesses the sensitivity of Land Use Land Cover (LULC) representation on the evolution of mesoscale convective systems over Bhubaneswar, a rapidly growing city (~ 77% growth in the last two decades) in India. In this study, three types of LULC maps have been prepared using supervised machine learning (ML) methods such as Classification and Regression Trees (CART), Naive Bayes (NB), and Support Vector Machine (SVM) on Google Earth Engine (GEE) platform using Landsat 8 for 2014. A high accuracy score (87%) and kappa coefficient (84%) revealed the best performance of CART in generating the LULC map. The Weather Research and Forecasting (WRF) model at 6 and 2 km horizontal resolution is forced with these LULC maps. Model results highlight that the CART experiment exhibits relatively less bias in 2 m relative humidity (~ – 10% to – 5%), 2 m temperature (~ 2.5 °C to ~ 0 °C), and 10 m wind speed (– 1 to ~ 1.8 m s−1) up to peak stage of the thunderstorms. The CART performs better with less rainfall error (~ – 16 mm) than CNTL (~ – 33 mm), NB (~ – 37 mm), and SVM (~ – 38 mm) and is supported by the quantitative statistical analysis, viz. less false alarm ratio, critical success index for different thresholds. LULC class-wise analysis indicates a higher variation in surface and lower atmospheric parameters over urban, shrubland, and cropland while less variation over barren, forest, and water. Thus, the study highlights the credibility of ML models in representing LULC information to input the high-resolution models.

Suggested Citation

  • Kumari Priya & Talukdar Sasanka & Krishna K. Osuri, 2023. "Land use land cover representation through supervised machine learning methods: sensitivity on simulation of urban thunderstorms in the east coast of India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 295-317, March.
  • Handle: RePEc:spr:nathaz:v:116:y:2023:i:1:d:10.1007_s11069-022-05674-4
    DOI: 10.1007/s11069-022-05674-4
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    References listed on IDEAS

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    1. S. Kiran Prasad & U. Mohanty & A. Routray & Krishna Osuri & S. Ramakrishna & Dev Niyogi, 2014. "Impact of Doppler weather radar data on thunderstorm simulation during STORM pilot phase—2009," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1403-1427, December.
    2. Kumari Priya & Raghu Nadimpalli & Krishna K. Osuri, 2021. "Do increasing horizontal resolution and downscaling approaches produce a skillful thunderstorm forecast?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(2), pages 1655-1674, November.
    3. Kotapati Narayana Loukika & Venkata Reddy Keesara & Venkataramana Sridhar, 2021. "Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
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