Remya Ravikumar, Pralay Sankar Maitra, Alka Singh, Nagesh K Subbana
{"title":"基于随机森林回归的喀拉拉邦海岸岸线变化人工神经网络检测和地下水变化","authors":"Remya Ravikumar, Pralay Sankar Maitra, Alka Singh, Nagesh K Subbana","doi":"10.1109/ICESC57686.2023.10193557","DOIUrl":null,"url":null,"abstract":"Shoreline change is a constantly evolving phenomenon that threatens people and their livelihoods around the globe. India observes this phenomenon strongly at different locations being a tropical peninsular country with 6635kms of coastline. This study analyzes the effect of shoreline along the entire coast of Kerala state in India. Net changes in coastline positions are statistically calculated and observed using Linear Regression Rate (LRR) and validated using Artificial Neural Network. The study also employes a random forest regression to predict the ground water level changes with respect to shoreline change rate in the region. The shoreline change rate shows most of the region are undergoing erosion, only few accretions or land formation are observed which is formed artificially due to harbor building. The highest erosion rate in terms of LRR is 7m/year and highest accretion is 28m/year.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Shore Line Change Detection using ANN and Ground Water Variability Along Kerala Coast Using Random Forest Regression\",\"authors\":\"Remya Ravikumar, Pralay Sankar Maitra, Alka Singh, Nagesh K Subbana\",\"doi\":\"10.1109/ICESC57686.2023.10193557\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Shoreline change is a constantly evolving phenomenon that threatens people and their livelihoods around the globe. India observes this phenomenon strongly at different locations being a tropical peninsular country with 6635kms of coastline. This study analyzes the effect of shoreline along the entire coast of Kerala state in India. Net changes in coastline positions are statistically calculated and observed using Linear Regression Rate (LRR) and validated using Artificial Neural Network. The study also employes a random forest regression to predict the ground water level changes with respect to shoreline change rate in the region. The shoreline change rate shows most of the region are undergoing erosion, only few accretions or land formation are observed which is formed artificially due to harbor building. The highest erosion rate in terms of LRR is 7m/year and highest accretion is 28m/year.\",\"PeriodicalId\":235381,\"journal\":{\"name\":\"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICESC57686.2023.10193557\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICESC57686.2023.10193557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Shore Line Change Detection using ANN and Ground Water Variability Along Kerala Coast Using Random Forest Regression
Shoreline change is a constantly evolving phenomenon that threatens people and their livelihoods around the globe. India observes this phenomenon strongly at different locations being a tropical peninsular country with 6635kms of coastline. This study analyzes the effect of shoreline along the entire coast of Kerala state in India. Net changes in coastline positions are statistically calculated and observed using Linear Regression Rate (LRR) and validated using Artificial Neural Network. The study also employes a random forest regression to predict the ground water level changes with respect to shoreline change rate in the region. The shoreline change rate shows most of the region are undergoing erosion, only few accretions or land formation are observed which is formed artificially due to harbor building. The highest erosion rate in terms of LRR is 7m/year and highest accretion is 28m/year.
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