Coastline and River Banks Erosion prediction using Image processing

Bhavan Gowda N, Manoj J, Mithun M, A R Manish Varma, Prof. Surekha Thota


In recent years the people are aware of the threat posed by river banks and coastal erosion. In several parts of the world, national and local governments are planning strategic management strategies in response. A thorough knowledge of coastal processes is an essential component of the planning process. Coastline erosion prediction is a process of estimating the future coastline and river banks. It is crucial to understand and predict large-scale, longer-term coastal changes, in order to manage the risk of 33 coastal settlements in India. The ecstatic sea level rise due to global warming is predicted to be about 18 to 59 cm by the 2100, which necessitates identification and protection of vulnerable sections of coasts. Research has been conducted for identification of coastal erosions and predicting changes in the coast line but none involves computerized technique for erosion prediction. Hence, we propose an innovative and practical method of predicting the changes in the coastline by using computer tools. The main aim of this project is to predict the coastline erosion efficiently by analyzing the images recorded through google earth. The absorbed images from google earth are feather processed and the transformed images are used for predicting the changes using SSIM tool.



Image processing, SSIM(structural similarity), Grey scale imaging, image transformation

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M. Djeddou, I. A. Hameed and E. Mokhtari, "Soil Erosion Rate Prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Geographic Information System (GIS) of Wadi Sahel-Soummam Watershed (Algeria)," 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA, 2019, pp. 1-7, doi: 10.1109/FUZZ-IEEE.2019.8858857.

W. Li et al., "ℓ0ell_0 Sparse Approximation of Coastline Inflection Method on FY-3C MWRI Data," in IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 1, pp. 85-89, Jan. 2019.

X. Hu and Y. Wang, "Coastlines Change of the Pearl River Estuary In The Past 40 Years Using Landsat Dataset And Its Environmental Implications," IGARSS 2019 - 2019 IEEE

F. Nunziata, A. Buono, M. Migliaccio, G. Benassai and D. D. Luccio, "Shoreline erosion of microtidal beaches examined with UAV and remote sensing techniques," 2018 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea), Bari, Italy, 2018, pp. 162-166.

X. Wu, C. Liu and G. Wu, "Spatial-Temporal Analysis and Stability Investigation of Coastline Changes: A Case Study in Shenzhen, China," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 1, pp. 45-56, Jan. 2018.

L. He, Q. Xu, H. Hu and J. Zhang, "Fast and Accurate Sea-Land Segmentation based on Improved SeNet and Coastline Database for Large-Scale Image," 2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Xi'an, 2018.

Ashwinkumar.U.M and Dr. Anandakumar K.R, "Predicting Early Detection of cardiac and Diabetes symptoms using Data mining techniques", International conference on computer Design and Engineering, vol.49, 2012

Maraş, Erdem & Caniberk, Mustafa & Maras, Hadi. (2016). Automatic Coastline Detection Using Image Enhancement and Segmentation Algorithms. Polish Journal of Environmental Studies. 25. 10.15244/pjoes/64160.



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