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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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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.

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Data and material availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The numerical modeling code and the data used are freely available and accessible.

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Acknowledgements

The work is part of the THUMP Project (No.MoES/16/09/2018-RDEAS-THUMP-7) and is supported by the Earth System Science Organization, Ministry of Earth Sciences, Govt. of India. The authors also acknowledge the European Center for Medium Range Weather Forecasts (ECMWF), National Aeronautical and Space Administration (NASA), and Iowa state university of Science and Technology for ERA5, GPM, and METAR data sets, respectively, to carry out this study. IMD is acknowledged for making storm reports available. Authors are grateful for the computational capacities of the Google Earth Engine.

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  1. Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India

    Kumari Priya, Talukdar Sasanka & Krishna K. Osuri

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KP took part in conceptualizations, methodology, data preparation, analysis, original draft preparation, reviewing, and editing. TS involved in conceptualizations, methodology, analysis, reviewing, and editing. KKO involved in conceptualizations, methodology, supervision, analysis, review, & editing.

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Correspondence to Krishna K. Osuri.

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Priya, K., Sasanka, T. & Osuri, K.K. Land use land cover representation through supervised machine learning methods: sensitivity on simulation of urban thunderstorms in the east coast of India. Nat Hazards 116, 295–317 (2023). https://doi.org/10.1007/s11069-022-05674-4

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