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<p>This study integrates local site effects and land use land cover dynamics using advanced statistical and machine learning methods into the probabilistic seismic hazard analysis (PSHA) framework for assessing the seismic hazard of Prakasam district, Andhra Pradesh, Peninsular India. An updated homogenized earthquake catalogue spanning from 1800 to 2023 AD, around a 400 km radius from Prakasam district, was considered for the analysis. A seismotectonic map was developed incorporating regional geology, past seismicity, and geological discontinuities that are capable of producing significant ground motion. Seismic source zones were delineated, and seismicity parameters were quantified using a Gutenberg–Richter recurrence model. Ground motion prediction equations (GMPEs) were ranked based on their alignment with recorded ground motion data using the log-likelihood (LLH) method. Spatial variations in hazard, expressed in terms of peak ground acceleration (PGA), were determined for return periods of 475 and 2475 yr. Surface-level PGA values for the study area ranged from 0.097 to 0.277 g for the 475-yr return period and from 0.233 to 0.539 g for the 2475-yr return period. Uniform hazard spectrum (UHS) for significant locations in Prakasam district was plotted and compared with IS 1893 (Part 1): 2016 codal provisions. Land use and land cover change detection from 2001 to 2024 was performed utilizing satellite images via supervised classifiers such as maximum likelihood (ML), support vector machine (SVM), and random forest (RF). Finally, an integrated land use and seismic hazard map was developed, offering valuable insights for urban planners, insurance companies, and policymakers. The outcomes of the present study aid in earthquake hazard mitigation efforts by facilitating informed decision-making and planning of resilient infrastructure.</p>

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Development of surface-level hazard maps incorporating land use land cover dynamics using statistical and machine learning approaches for Prakasam district, Peninsular India

  • 期刊论文

  • 发表日期：

    2025年8月31日

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  • R Siddhardha,
  • Kalyan Kumar Gonavaram

摘要

This study integrates local site effects and land use land cover dynamics using advanced statistical and machine learning methods into the probabilistic seismic hazard analysis (PSHA) framework for assessing the seismic hazard of Prakasam district, Andhra Pradesh, Peninsular India. An updated homogenized earthquake catalogue spanning from 1800 to 2023 AD, around a 400 km radius from Prakasam district, was considered for the analysis. A seismotectonic map was developed incorporating regional geology, past seismicity, and geological discontinuities that are capable of producing significant ground motion. Seismic source zones were delineated, and seismicity parameters were quantified using a Gutenberg–Richter recurrence model. Ground motion prediction equations (GMPEs) were ranked based on their alignment with recorded ground motion data using the log-likelihood (LLH) method. Spatial variations in hazard, expressed in terms of peak ground acceleration (PGA), were determined for return periods of 475 and 2475 yr. Surface-level PGA values for the study area ranged from 0.097 to 0.277 g for the 475-yr return period and from 0.233 to 0.539 g for the 2475-yr return period. Uniform hazard spectrum (UHS) for significant locations in Prakasam district was plotted and compared with IS 1893 (Part 1): 2016 codal provisions. Land use and land cover change detection from 2001 to 2024 was performed utilizing satellite images via supervised classifiers such as maximum likelihood (ML), support vector machine (SVM), and random forest (RF). Finally, an integrated land use and seismic hazard map was developed, offering valuable insights for urban planners, insurance companies, and policymakers. The outcomes of the present study aid in earthquake hazard mitigation efforts by facilitating informed decision-making and planning of resilient infrastructure.