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Year 2025, Volume: 7 Issue: 1, 107 - 124, 30.06.2025
https://doi.org/10.51489/tuzal.1636620

Abstract

References

  • Ahsan, R. (2019) Climate induced migration: impacts on social structure and justice in Bangladesh. The International Journal of Climate Change: Impacts and Responses, 39(2), 184-201. https://doi.org/10.1177/0262728019842968
  • Aleem, M., Ahmed, S. A., & H, N. (2023). Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, 16, 3057–3073. https://doi.org/10.1007/s12145-023-01073-w
  • Alptekin, A. & Taga, H. (2019). Prediction of compression and swelling index parameters of Quaternary sediments from index tests at Mersin district. Open Geosciences, 11(1), 482-491. https://doi.org/10.1515/geo-2019-0038
  • Armah, F. A., Yawson, D. O., Yengoh, G. T., Odoi, J. O., & Afrifa, E.K.A. (2010) Impact of floods on livelihoods and vulnerability of natural resource dependent communities in Northern Ghana. Water, 2, 120–139. https://doi.org/10.3390/w2020120
  • Arpitha, M., Ahmed, S. A., & Harishnaika, N. (2023). Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, 16, 3057–3073. https://doi.org/10.1007/s12145-023-01073-w
  • Aryal, J., Sitaula, C., & Frery, A. C. (2023). Land use and land cover (LULC) performance modeling using machine learning algorithms: A case study of the city of Melbourne, Australia. Scientific Reports, 13, 1351. https://doi.org/10.1038/s41598-023-40564-0
  • Barros, D. J., Dokken, K. J., Mach, M. D., Mastrandrea, T. E., Bilir, M., Chatterjee, K. L., Ebi, Y. O., Estrada, R. C., Genova, B., Girma, E. S., Kissel, A. N., Levy, S., MacCracken, P. R., Mastrandrea,, & White, L. L. (2014). Climate change 2014: Impact, adaptation and vernability. Cambridge University Press.
  • Billa, L., Shattri, M., Mahmud, A.R., & Ghazali, A.H. (2006) Comprehensive planning and the role of SDSS in flood disaster management in Malaysia. Disaster Prevention Management An International Journal 15(2), 233–240. https://doi.org/10.1108/09653560610659775
  • Brouwer, R., Akter, S., Brander, L., & Haque, E. (2007). Socioeconomic vulnerability and adaptation to environmental risk: a case study of climate change and flooding in Bangladesh. Risk Analysis: An International Journal, 27(2), 313-326. https://doi.org/10.1111/j.1539-6924.2007.00884.x
  • Bruckner, M.Z. (2012) Water and soil characterization- pH and electrical conductivity. Microbial Life Educational Resources. Montana State University Bozeman.
  • Cash, R. A., Halder, S. R., Husain, M., Islam, M. S., Mallick, F. H., May, M. A., Rahman, M., & Rahman, M. A. (2013). Reducing the health effect of natural hazards in Bangladesh. The Lancet, 382(9910), 2094-2103. http://dx.doi.org/10.1016/S0140-6736(13)61948-0
  • CIRDAP. (1991). Center on Integrated Rural Development for Asia and the Pacific (CIRDAP). Development of modules for training on integrated approach to rural development and disaster management in Bangladesh, Final Report of UNCRD-CIRDAP.
  • Gray, C. L. (2011). Soil quality and human migration in Kenya and Uganda. Global Environmental Change, 21(2), 421-430. https://doi.org/10.1016/j.gloenvcha.2011.02.004
  • Habiba, U., Abedin, M. A., Shaw, R., & Hassan, A. W. R. (2014). Salinity-induced livelihood stress in coastal region of Bangladesh. In water insecurity: A social dilemma. Emerald Group Publishing Limited.
  • Halder, B.; Das, S.; Bandyopadhyay, J.; Banik,P.(2021) The deadliest tropical cyclone ‘Amphan’: investigate the natural flood inundation over south 24 Parganas using google earth engine. Springer Nature. https://doi.org/10.1007/s42797-021-00035-z
  • Huang, X., Tan, H., Zhou, J., Yang, T., Benjamin, A., Wen, S. W., Li, S., Li, X., Fen, S., & Li, X. (2008). Flood hazard in Hunan province of China: an economic loss analysis. Natural Hazards, 47, 65-73. https://doi.org/10.1007/s11069-007-9197-z
  • Iban, M. C. (2020). Geospatial data science response to COVID-19 crisis and pandemic isolation tracking. Turkish Journal of Geosciences, 1(1), 1-7.
  • Johnson, J. D. (2006) Natural disaster and vulnerability; OECD development center policy Brief No. 29. OECD Development Center: Berlin, Germany. https://doi.org/10.1787/202670544086
  • Karl, T. R., Melillo, J. M., & Peterson. T.C. (2009). USGCRP global climate change impacts in the United States, coasts United States Global Change Research Program. Cambridge University Press.
  • Lavender, S. L., Hoeke, R. K., & Abbs, D. J. (2018). The influence of sea surface temperature on the intensity and associated storm surge of tropical cyclone Yasi: a sensitivity study. Natural Hazards and Earth System Sciences, 18(3), 795-805. https://doi.org/10.5194/nhess-18-795-2018
  • Loukika, K. N., Keesara, V. R., & Sridhar, V. (2021). Analysis of land use and land cover using machine learning algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability, 13, 13758. https://doi.org/10.3390/su132413758
  • Mahadevia, K., & Vikas, M. (2012). Climate change–impact on the Sundarbans, a case study. International Scientific Journal: Environmental Science, 2(1), 7-15.
  • Mallick, B., Rahaman, K. R., & Vogt, J. (2011). Coastal livelihood and physical infrastructure in Bangladesh after cyclone Aila. Mitigation and adaptation strategies for global change, 16, 629-648. https://doi.org/10.1007/s11027-011-9285-y
  • Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784–2817. https://doi.org/10.1080/01431161.2018.1433343
  • Messner, F., & Meyer, V. (2006) Flood damage, vulnerability and risk perception-challenges for flood damage research. Springer.
  • Mondal, M. (2012) Land people - a dynamic interaction of Purba Medinipur district, West Benga. IOSR Journal of Pharmacy 2(6), 56-61. https://doi.org/10.9790/3013-26405661
  • Mondal, M., Dandapath, P. K., & Shukla, J. (2013). Mapping Dynamics of land utilization and its changing Patterns of Purba Medinipure District-WB. International Journal of Innovative Research and Development, 2(1), 664-676.
  • Moser, S. C., Davidson, M. A., Kirshen, P., Mulvaney, P., Murley, J. F., Neumann, J. E., Petes, L., & Reed, D. (2014). Ch. 25: coastal zone development and ecosystems. Climate Change Impacts in the United States: The Third National Climate Assessment, JM Melillo, Terese (TC) Richmond, and GW Yohe, Eds., US Global Change Research Program, 579-618.
  • Nandargi, S. S., & Mahto, S. S. (2019). Frequency and intensity of tropical disturbances over the Indian region and its neighboring seas with associated rainfall during the monsoon season: A perspective. Engineering Reports, 1(5), e12069. https://doi.org/10.1002/eng2.12069
  • Nandargi, S., & Dhar, O. N. (1998). An appraisal of successive tropical disturbances and their associated severe rainstorms during monsoon months. Journal of Meteorology, 23(231), 221-228.
  • NRC. (2010). National Research Council (NRC). Adapting to the Impacts of Climate Change. (NRC). The National Academies Press.
  • Parvin, G. A., & Shaw, R. (2013). Microfinance institutions and a coastal community's disaster risk reduction, response, and recovery process: a case study of Hatiya, Bangladesh. Disasters, 37(1), 165-184. https://doi.org/10.1111/j.1467-7717.2012.01292.x
  • Parvin, G. A., Shimi, A. C., Shaw, R., & Biswas, C. (2016). Flood in a changing climate: The impact on livelihood and how the rural poor cope in Bangladesh. Climate, 4(4), 60. https://doi.org/10.3390/cli4040060
  • Paul, S. K., & Routray, J. K. (2010). Flood proneness and coping strategies: the experiences of two villages in Bangladesh. Disasters, 34(2), 489-508. https://doi.org/10.1111/j.1467-7717.2009.01139.x
  • Rahman, S., Rahman, H., Shahid, S., Khan, R. U., Jahan, N., Ahmed, Z. U., ... & Mohsenipour, M. (2017). The impact of cyclone Aila on the Sundarban forest ecosystem. International Journal of Ecology and Development, 32(1), 87-97.
  • Rahman, S., Rahman, H., Shahid, S., Khan, R. U., Jahan, N., Ahmed, Z. U., Khanum, R., Ahmed, M. F., Ahsan, R., Kellett, J., Karuppannan, S. (2014). Responses to drought and desertification in the Moroccan Drâa Valley Region: Resilience at the expense of sustainability? The International Journal of Climate Change: Impacts and Responses, 5(2), 17.
  • Rayhan, M. I. (2010). Assessing poverty, risk and vulnerability: a study on flooded households in rural Bangladesh. Journal of Flood Risk Management, 3(1), 18-24. https://doi.org/10.1111/j.1753-318X.2009.01051.x
  • Rhein, M., Rintoul, S. R., Aoki, S., Campos, E., Chambers, D., Feely, R. A., Gulev, S., Johnson, G. C., Josey, S. A., Kostianoy, A., Mauritzen, C., Roemmich, D., Talley, L. D., & Wang, F. (2013) Observations: Ocean In Climate Change 2013 IPCC: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change ,Cambridge University Press.
  • Sadik, M. S., Nakagawa, H., Rahman, M. R., Shaw, R., Kawaike, K., Parvin, G. A., & Fujita, K. (2018). Humanitarian aid driven recovery of housing after Cyclone Aila in Koyra, Bangladesh: Characterization and assessment of outcome. Journal of Japan Society for Natural Disaster Science, 37(S05), 73-91. https://doi.org/10.24762/jndsj.37.S05_73
  • Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x
  • Sinha, S., Santra, A., & Mitra, S. S. (2020). Semi-automated impervious feature extraction using built-up indices developed from space-borne optical and SAR remotely sensed sensors. Advances in Space Research, 66(6), 1372-1385. https://doi.org/10.1016/j.asr.2020.05.040
  • Sinha, S., Sharma, L. K., & Nathawat, M. S. (2013). Integrated geospatial techniques for land-use/land-cover and forest mapping of deciduous Munger forests (India). Universal Journal of Environmental Research & Technology, 3(2), 190-198.
  • Sinha, S., Sharma, L. K., & Nathawat, M. S. (2015). Improved Land-use/Land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 217-233. https://doi.org/10.1016/j.ejrs.2015.09.005
  • Subhani, R., & Ahmad, M. M. (2019). Socio-economic impacts of cyclone aila on migrant and non-migrant households in the southwestern coastal areas of Bangladesh. Geosciences, 9(11), 482. https://doi.org/10.3390/geosciences9110482
  • Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135
  • UNISDR. (2013). United Nations International Strategy for Disaster Reduction (UNISDR). In HFA-Asia Pacific, (2011–2013), Hyogo Framework for Action in Asia and the Pacific; UNISDR.
  • United Nations. (2009) Risk and Poverty in Changing Climate Global Assessment Report on Disaster Risk Reduction; United Nations: Geneva, Switzerland.
  • Walsh, J., Wuebbles, D., Hayhoe, K., Kossin, J., Kunkel, K., Stephens, G., ... & Somerville, R. (2014). Ch. 2: Our changing climate. Climate change impacts in the United States: The third national climate assessment, 19-67.
  • Wang, Q. B., Xu, W., Xue, Q. Z., & Su, W. A. (2010). Transgenic Brassica chinensis plants expressing a bacterial codA gene exhibit enhanced tolerance to extreme temperature and high salinity. Journal of Zhejiang University Science B, 11(11), 851-861. https://doi.org/10.1631/jzus.B1000137
  • Wong, P. P., Losada, I. J., Gattuso, J. P., Hinkel, J., Khattabi, A., McInnes, K. L., Saito, Y., & Sallenger, A. (2014). Coastal systems and low-lying areas. Climate change, 2104, 361-409.
  • Yodmani, S. (2001). Disaster risk management and vulnerability reduction: Protecting the poor. New York: The Center.
  • Younus, M. A. F., & Harvey, N. (2014). Economic consequences of failed autonomous adaptation to extreme floods: A case study from Bangladesh. Local Economy, 29(1-2), 22-37. https://doi.org/10.1177/0269094213515175
  • Younus, M.A.; Sharna, S.S.; Rahman, T.B. (2013, November) Integrated assessment and decision-support tool for community-based vulnerability and adaptation to storm surges in four coastal areas in Bangladesh [Paper presentation]. Proceedings of the Australia New Zealand Society for Ecological Economics 2013 Conference, Canberra, Australia.
  • Zafar, Z., Zubair, M., Zha, Y., Fahd, S., & Nadeem, A. A. (2024). Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data. The Egyptian Journal of Remote Sensing and Space Sciences, 27(2), 216-226.

Impact of climate change induced hazards on the livelihood of marginalized coastal dwellers of West Bengal, India

Year 2025, Volume: 7 Issue: 1, 107 - 124, 30.06.2025
https://doi.org/10.51489/tuzal.1636620

Abstract

The tropical coastal regions are severely vulnerable to climate change and associated hazards, having a major impact on global hydrological cycle from the last three decades, with subsequent enhancement of hazards. Tidal inundations and the saline water intrusions are responsible for the disaster in coastal areas of West Bengal making people susceptible to livelihood hazards by affecting the agricultural land resources which transform into saline land resulting in joblessness and income reduction. Storm Surge and cyclonic high-speed wind break the weak river embankments, resulting in inundations. So, the aim of the study is to unfold the effect of inundation due to super cyclone Amphan and Yash on the livelihood of the rural people, in Ramnagar I & II Blocks, of Purba Medinipur, West Bengal, India. NDVI during the years 2000, 2010 and 2022 reveal a decline in the values, ranging from -0.54 to +0.7 in 2000, -0.239 to +0.55 in 2010, and -0.16 to +0.51 in 2022, showing a gradual reduction in agricultural productivity during 2000 to 2022. Notably, the most pronounced changes in NDVI values occur between the coastline and 25 to 30 kilometers inland, an area where soil salinity has had a detrimental impact on the land. NDVI values have steadily decreased from 2000 to 2022, indicating reduced agricultural productivity. Agricultural land has decreased by 62.36%, while water proportions increased by 71.2% and rural settlements decreased by 37.64% due to water inundations during the period 2010 to 2022. Likewise, 6% of the respondents became jobless during post-disaster phase.

References

  • Ahsan, R. (2019) Climate induced migration: impacts on social structure and justice in Bangladesh. The International Journal of Climate Change: Impacts and Responses, 39(2), 184-201. https://doi.org/10.1177/0262728019842968
  • Aleem, M., Ahmed, S. A., & H, N. (2023). Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, 16, 3057–3073. https://doi.org/10.1007/s12145-023-01073-w
  • Alptekin, A. & Taga, H. (2019). Prediction of compression and swelling index parameters of Quaternary sediments from index tests at Mersin district. Open Geosciences, 11(1), 482-491. https://doi.org/10.1515/geo-2019-0038
  • Armah, F. A., Yawson, D. O., Yengoh, G. T., Odoi, J. O., & Afrifa, E.K.A. (2010) Impact of floods on livelihoods and vulnerability of natural resource dependent communities in Northern Ghana. Water, 2, 120–139. https://doi.org/10.3390/w2020120
  • Arpitha, M., Ahmed, S. A., & Harishnaika, N. (2023). Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, 16, 3057–3073. https://doi.org/10.1007/s12145-023-01073-w
  • Aryal, J., Sitaula, C., & Frery, A. C. (2023). Land use and land cover (LULC) performance modeling using machine learning algorithms: A case study of the city of Melbourne, Australia. Scientific Reports, 13, 1351. https://doi.org/10.1038/s41598-023-40564-0
  • Barros, D. J., Dokken, K. J., Mach, M. D., Mastrandrea, T. E., Bilir, M., Chatterjee, K. L., Ebi, Y. O., Estrada, R. C., Genova, B., Girma, E. S., Kissel, A. N., Levy, S., MacCracken, P. R., Mastrandrea,, & White, L. L. (2014). Climate change 2014: Impact, adaptation and vernability. Cambridge University Press.
  • Billa, L., Shattri, M., Mahmud, A.R., & Ghazali, A.H. (2006) Comprehensive planning and the role of SDSS in flood disaster management in Malaysia. Disaster Prevention Management An International Journal 15(2), 233–240. https://doi.org/10.1108/09653560610659775
  • Brouwer, R., Akter, S., Brander, L., & Haque, E. (2007). Socioeconomic vulnerability and adaptation to environmental risk: a case study of climate change and flooding in Bangladesh. Risk Analysis: An International Journal, 27(2), 313-326. https://doi.org/10.1111/j.1539-6924.2007.00884.x
  • Bruckner, M.Z. (2012) Water and soil characterization- pH and electrical conductivity. Microbial Life Educational Resources. Montana State University Bozeman.
  • Cash, R. A., Halder, S. R., Husain, M., Islam, M. S., Mallick, F. H., May, M. A., Rahman, M., & Rahman, M. A. (2013). Reducing the health effect of natural hazards in Bangladesh. The Lancet, 382(9910), 2094-2103. http://dx.doi.org/10.1016/S0140-6736(13)61948-0
  • CIRDAP. (1991). Center on Integrated Rural Development for Asia and the Pacific (CIRDAP). Development of modules for training on integrated approach to rural development and disaster management in Bangladesh, Final Report of UNCRD-CIRDAP.
  • Gray, C. L. (2011). Soil quality and human migration in Kenya and Uganda. Global Environmental Change, 21(2), 421-430. https://doi.org/10.1016/j.gloenvcha.2011.02.004
  • Habiba, U., Abedin, M. A., Shaw, R., & Hassan, A. W. R. (2014). Salinity-induced livelihood stress in coastal region of Bangladesh. In water insecurity: A social dilemma. Emerald Group Publishing Limited.
  • Halder, B.; Das, S.; Bandyopadhyay, J.; Banik,P.(2021) The deadliest tropical cyclone ‘Amphan’: investigate the natural flood inundation over south 24 Parganas using google earth engine. Springer Nature. https://doi.org/10.1007/s42797-021-00035-z
  • Huang, X., Tan, H., Zhou, J., Yang, T., Benjamin, A., Wen, S. W., Li, S., Li, X., Fen, S., & Li, X. (2008). Flood hazard in Hunan province of China: an economic loss analysis. Natural Hazards, 47, 65-73. https://doi.org/10.1007/s11069-007-9197-z
  • Iban, M. C. (2020). Geospatial data science response to COVID-19 crisis and pandemic isolation tracking. Turkish Journal of Geosciences, 1(1), 1-7.
  • Johnson, J. D. (2006) Natural disaster and vulnerability; OECD development center policy Brief No. 29. OECD Development Center: Berlin, Germany. https://doi.org/10.1787/202670544086
  • Karl, T. R., Melillo, J. M., & Peterson. T.C. (2009). USGCRP global climate change impacts in the United States, coasts United States Global Change Research Program. Cambridge University Press.
  • Lavender, S. L., Hoeke, R. K., & Abbs, D. J. (2018). The influence of sea surface temperature on the intensity and associated storm surge of tropical cyclone Yasi: a sensitivity study. Natural Hazards and Earth System Sciences, 18(3), 795-805. https://doi.org/10.5194/nhess-18-795-2018
  • Loukika, K. N., Keesara, V. R., & Sridhar, V. (2021). Analysis of land use and land cover using machine learning algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability, 13, 13758. https://doi.org/10.3390/su132413758
  • Mahadevia, K., & Vikas, M. (2012). Climate change–impact on the Sundarbans, a case study. International Scientific Journal: Environmental Science, 2(1), 7-15.
  • Mallick, B., Rahaman, K. R., & Vogt, J. (2011). Coastal livelihood and physical infrastructure in Bangladesh after cyclone Aila. Mitigation and adaptation strategies for global change, 16, 629-648. https://doi.org/10.1007/s11027-011-9285-y
  • Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784–2817. https://doi.org/10.1080/01431161.2018.1433343
  • Messner, F., & Meyer, V. (2006) Flood damage, vulnerability and risk perception-challenges for flood damage research. Springer.
  • Mondal, M. (2012) Land people - a dynamic interaction of Purba Medinipur district, West Benga. IOSR Journal of Pharmacy 2(6), 56-61. https://doi.org/10.9790/3013-26405661
  • Mondal, M., Dandapath, P. K., & Shukla, J. (2013). Mapping Dynamics of land utilization and its changing Patterns of Purba Medinipure District-WB. International Journal of Innovative Research and Development, 2(1), 664-676.
  • Moser, S. C., Davidson, M. A., Kirshen, P., Mulvaney, P., Murley, J. F., Neumann, J. E., Petes, L., & Reed, D. (2014). Ch. 25: coastal zone development and ecosystems. Climate Change Impacts in the United States: The Third National Climate Assessment, JM Melillo, Terese (TC) Richmond, and GW Yohe, Eds., US Global Change Research Program, 579-618.
  • Nandargi, S. S., & Mahto, S. S. (2019). Frequency and intensity of tropical disturbances over the Indian region and its neighboring seas with associated rainfall during the monsoon season: A perspective. Engineering Reports, 1(5), e12069. https://doi.org/10.1002/eng2.12069
  • Nandargi, S., & Dhar, O. N. (1998). An appraisal of successive tropical disturbances and their associated severe rainstorms during monsoon months. Journal of Meteorology, 23(231), 221-228.
  • NRC. (2010). National Research Council (NRC). Adapting to the Impacts of Climate Change. (NRC). The National Academies Press.
  • Parvin, G. A., & Shaw, R. (2013). Microfinance institutions and a coastal community's disaster risk reduction, response, and recovery process: a case study of Hatiya, Bangladesh. Disasters, 37(1), 165-184. https://doi.org/10.1111/j.1467-7717.2012.01292.x
  • Parvin, G. A., Shimi, A. C., Shaw, R., & Biswas, C. (2016). Flood in a changing climate: The impact on livelihood and how the rural poor cope in Bangladesh. Climate, 4(4), 60. https://doi.org/10.3390/cli4040060
  • Paul, S. K., & Routray, J. K. (2010). Flood proneness and coping strategies: the experiences of two villages in Bangladesh. Disasters, 34(2), 489-508. https://doi.org/10.1111/j.1467-7717.2009.01139.x
  • Rahman, S., Rahman, H., Shahid, S., Khan, R. U., Jahan, N., Ahmed, Z. U., ... & Mohsenipour, M. (2017). The impact of cyclone Aila on the Sundarban forest ecosystem. International Journal of Ecology and Development, 32(1), 87-97.
  • Rahman, S., Rahman, H., Shahid, S., Khan, R. U., Jahan, N., Ahmed, Z. U., Khanum, R., Ahmed, M. F., Ahsan, R., Kellett, J., Karuppannan, S. (2014). Responses to drought and desertification in the Moroccan Drâa Valley Region: Resilience at the expense of sustainability? The International Journal of Climate Change: Impacts and Responses, 5(2), 17.
  • Rayhan, M. I. (2010). Assessing poverty, risk and vulnerability: a study on flooded households in rural Bangladesh. Journal of Flood Risk Management, 3(1), 18-24. https://doi.org/10.1111/j.1753-318X.2009.01051.x
  • Rhein, M., Rintoul, S. R., Aoki, S., Campos, E., Chambers, D., Feely, R. A., Gulev, S., Johnson, G. C., Josey, S. A., Kostianoy, A., Mauritzen, C., Roemmich, D., Talley, L. D., & Wang, F. (2013) Observations: Ocean In Climate Change 2013 IPCC: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change ,Cambridge University Press.
  • Sadik, M. S., Nakagawa, H., Rahman, M. R., Shaw, R., Kawaike, K., Parvin, G. A., & Fujita, K. (2018). Humanitarian aid driven recovery of housing after Cyclone Aila in Koyra, Bangladesh: Characterization and assessment of outcome. Journal of Japan Society for Natural Disaster Science, 37(S05), 73-91. https://doi.org/10.24762/jndsj.37.S05_73
  • Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x
  • Sinha, S., Santra, A., & Mitra, S. S. (2020). Semi-automated impervious feature extraction using built-up indices developed from space-borne optical and SAR remotely sensed sensors. Advances in Space Research, 66(6), 1372-1385. https://doi.org/10.1016/j.asr.2020.05.040
  • Sinha, S., Sharma, L. K., & Nathawat, M. S. (2013). Integrated geospatial techniques for land-use/land-cover and forest mapping of deciduous Munger forests (India). Universal Journal of Environmental Research & Technology, 3(2), 190-198.
  • Sinha, S., Sharma, L. K., & Nathawat, M. S. (2015). Improved Land-use/Land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 217-233. https://doi.org/10.1016/j.ejrs.2015.09.005
  • Subhani, R., & Ahmad, M. M. (2019). Socio-economic impacts of cyclone aila on migrant and non-migrant households in the southwestern coastal areas of Bangladesh. Geosciences, 9(11), 482. https://doi.org/10.3390/geosciences9110482
  • Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135
  • UNISDR. (2013). United Nations International Strategy for Disaster Reduction (UNISDR). In HFA-Asia Pacific, (2011–2013), Hyogo Framework for Action in Asia and the Pacific; UNISDR.
  • United Nations. (2009) Risk and Poverty in Changing Climate Global Assessment Report on Disaster Risk Reduction; United Nations: Geneva, Switzerland.
  • Walsh, J., Wuebbles, D., Hayhoe, K., Kossin, J., Kunkel, K., Stephens, G., ... & Somerville, R. (2014). Ch. 2: Our changing climate. Climate change impacts in the United States: The third national climate assessment, 19-67.
  • Wang, Q. B., Xu, W., Xue, Q. Z., & Su, W. A. (2010). Transgenic Brassica chinensis plants expressing a bacterial codA gene exhibit enhanced tolerance to extreme temperature and high salinity. Journal of Zhejiang University Science B, 11(11), 851-861. https://doi.org/10.1631/jzus.B1000137
  • Wong, P. P., Losada, I. J., Gattuso, J. P., Hinkel, J., Khattabi, A., McInnes, K. L., Saito, Y., & Sallenger, A. (2014). Coastal systems and low-lying areas. Climate change, 2104, 361-409.
  • Yodmani, S. (2001). Disaster risk management and vulnerability reduction: Protecting the poor. New York: The Center.
  • Younus, M. A. F., & Harvey, N. (2014). Economic consequences of failed autonomous adaptation to extreme floods: A case study from Bangladesh. Local Economy, 29(1-2), 22-37. https://doi.org/10.1177/0269094213515175
  • Younus, M.A.; Sharna, S.S.; Rahman, T.B. (2013, November) Integrated assessment and decision-support tool for community-based vulnerability and adaptation to storm surges in four coastal areas in Bangladesh [Paper presentation]. Proceedings of the Australia New Zealand Society for Ecological Economics 2013 Conference, Canberra, Australia.
  • Zafar, Z., Zubair, M., Zha, Y., Fahd, S., & Nadeem, A. A. (2024). Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data. The Egyptian Journal of Remote Sensing and Space Sciences, 27(2), 216-226.
There are 54 citations in total.

Details

Primary Language English
Subjects Geospatial Information Systems and Geospatial Data Modelling
Journal Section Research Articles
Authors

Swarnali Mukhopadhyay 0000-0003-2797-6882

Gupinath Bhandari 0000-0002-0019-1420

Suman Sinha 0000-0001-6225-7379

Publication Date June 30, 2025
Submission Date February 19, 2025
Acceptance Date May 13, 2025
Published in Issue Year 2025 Volume: 7 Issue: 1

Cite

IEEE S. Mukhopadhyay, G. Bhandari, and S. Sinha, “Impact of climate change induced hazards on the livelihood of marginalized coastal dwellers of West Bengal, India”, TJRS, vol. 7, no. 1, pp. 107–124, 2025, doi: 10.51489/tuzal.1636620.

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