REVIEW OF THE STUDY CLIMATE CHANGE IMPACT ASSESSMENT ON THE GUJARAT COASTLINE: THE ROLE OF ARTIFICIAL INTELLIGENCE, STATISTICAL, MATHEMATICAL AND GEOGRAPHIC INFORMATION SYSTEMS (GIS)
Keywords:Climate change , Python programming language,Statistical analysisMathematical modelling Artificial Intelligence (AI), Data visualisation, Sea surface temperature,Coastal environments Gujarat coastline.
The impact of climate change is a growing concern for many societies, and there is a pressing need for accurate and reliable models that can predict the future of the climate system. Using artificial intelligence (AI) methods like machine learning and deep learning to evaluate and model climate data is one potential strategy. With an emphasis on statistical, mathematical, Python, and GIS-based studies on the Gujarat coastline, this study offers a summary of the current state of AI applications in climate change research. We highlight the promise of these techniques for expanding our understanding of climate change and creating efficient ways for tackling it as we present instances of AI techniques applied in climate modelling, environmental monitoring, and weather forecasting.
The paper gives a thorough list of pertinent references for people who want to learn more about how statistical, mathematics, GIS, and artificial intelligence techniques are used in climate change research. These sources cover a wide range of subjects, including the use of neural networks in climate modelling and the use of machine learning techniques for the study of satellite data. The paper serves as a useful resource for anybody wishing to further study the interface of AI and climate change research by giving this list of resources. With the help of these references, readers can get knowledge of the most recent advancements in the subject and see examples of how AI methods are being applied to improve our understanding of climate change and its effects.
Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological), 26(2), 211-252.
IPCC (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
Meier, M. F., Dyurgerov, M. B., Rick, U. K., O'Neel, S., Pfeffer, W. T., Anderson, R. S., ... & Young, N. E. (2007). Glaciers dominate eustatic sea-level rise in the 21st century. Science, 317(5841), 1064-1067.
McKinney, W., & others. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference pp. 51-56.
ESRI (2018). ArcGIS Desktop: Release 10.6.1. Environmental Systems Research Institute.
QGIS Development Team (2021). QGIS Geographic Information System. Qgis.org.
Dutta, D., & Chakraborty, A. (2019). Developing a machine learning-based approach for modeling the spatio-temporal dynamics of sea surface temperature in the Gulf of Kutch, India. International Journal of Remote Sensing, 40(13), 5015-5031.
Ghosh, D., & Dutta, D. (2019). Spatiotemporal analysis of precipitation trend in the Kutch region, India, using geospatial and statistical techniques. Natural Hazards, 97(3), 1213-1233.
Guo, H., Li, Y., Wang, J., Li, X., & Li, Y. (2021). Spatiotemporal changes in land surface temperature in China from 2003 to 2017 and their responses to climate change. Science of the Total Environment, 750, 141525.
Karmacharya, M., Shrestha, R. P., Bajracharya, B., & Shrestha, S. (2019). Vulnerability assessment of agriculture to climate change using GIS-based approaches: a case study of Kailali district, Nepal. Journal of Environmental Management, 245, 308-317.
Karmakar, S., & Chakraborty, A. (2017). An integrated approach to assess the vulnerability of coastal aquifers to climate change-induced seawater intrusion. Journal of Hydrology, 552, 181-194.
Mathur, M. B., Gohil, B. S., & Vyas, N. K. (2020). Land use land cover change assessment using remote sensing and GIS in coastal area of Gujarat, India. Journal of Environmental Management, 269, 110779.
Prajapati, J. B., & Dabhi, V. K. (2020). Analysis of climate variability and trend in coastal region of Saurashtra, Gujarat using Mann-Kendall and Sen's slope method. Environmental Science and Pollution Research, 27(1), 937-952.
Samanta, S., & Ghosh, D. (2021). Impacts of climate change on rice yield in coastal regions of Gujarat, India: An econometric analysis. Agricultural Economics Research Review, 34(2), 223-233.
Shang, S., Sun, X., Qiao, H., Cao, L., Ma, Z., & Liu, W. (2020). Impact of climate change on the distribution and abundance of wetland birds in the Yellow River Delta, China. Ecological Indicators, 110, 105926.
Singh, R., Singh, R. P., & Singh, A. K. (2018). Impact of climate change on irrigation water requirement and crop yield in Kutch region, India. Agricultural Water Management, 200, 87-98.
Tomar, S., & Singh, S. (2020). Performance evaluation of machine learning algorithms for classification of land use and land cover in Gulf of Kachchh, Gujarat. Arabian Journal of Geosciences, 13(22), 1251.
Vyas, N. K., & Patel, B. H. (2019). Mapping the impact of climate change on coastal environment of Gujarat using GIS and remote sensing. Journal of the Indian Society of Remote Sensing, 47(1), 31-43.
Yadav, R., Mishra, S. K., & Singh, S. (2021). Spatio-temporal variability of Indian summer monsoon rainfall over Gujarat state, India: A trend analysis. Atmospheric Research, 249, 105378.