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

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI:10.1109/ICESC57686.2023.10193557

Remya Ravikumar, Pralay Sankar Maitra, Alka Singh, Nagesh K Subbana

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引用次数: 0

摘要

海岸线变化是一个不断演变的现象，威胁着全球人民及其生计。印度是一个拥有6635公里海岸线的热带半岛国家，在不同的地方都能观察到这种强烈的现象。本研究分析了沿印度喀拉拉邦整个海岸的海岸线的影响。利用线性回归率(LRR)对海岸线位置的净变化进行了统计计算和观测，并利用人工神经网络进行了验证。该研究还采用随机森林回归来预测该地区地下水位变化与海岸线变化率的关系。海岸线变化率显示，大部分地区正在发生侵蚀，只有少数因建港而人工形成的增生或陆地形成。以LRR计算的最大侵蚀速率为7m/年，最大增生速率为28m/年。

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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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2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)

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