Mapping shoreline change using machine learning: a case study from the eastern Indian coast

• Autorzy: Kumar Lalit , Afzal Mohammad Saud , Afzal Mohammad Mashhood

Identyfikatory

DOI

10.1007/s11600-020-00454-9

• Języki publikacji: EN

Abstrakty

EN

The continuous shift of shoreline boundaries due to natural or anthropogenic events has created the necessity to monitor the shoreline boundaries regularly. This study investigates the perspective of implementing artifcial intelligence techniques to model and predict the realignment in shoreline along the eastern Indian coast of Orissa (now called Odisha). The modeling consists of analyzing the satellite images and corresponding reanalysis data of the coastline. The satellite images (Landsat imagery) of the Orissa coastline were analyzed using edge detection flters, mainly Sobel and Canny. Sobel and canny flters use edge detection techniques to extract essential information from satellite images. Edge detection reduces the volume of data and flters out worthless information while securing signifcant structural features of satellite images. The image diferencing technique is used to determine the shoreline shift from GIS images (Landsat imagery). The shoreline shift dataset obtained from the GIS image is used together with the metrological dataset extracted from Modern-Era Retrospective analysis for Research and Applications, Version 2, and tide and wave parameter obtained from the European Centre for Medium-Range Weather Forecast for the period 1985–2015, as input parameter in machine learning (ML) algorithms to predict the shoreline shift. Artifcial neural network (ANN), k-nearest neighbors (KNN), and support vector machine (SVM) algorithm are used as a ML model in the present study. The ML model contains weights that are multiplied with relevant inputs/features to obtain a better prediction. The analysis shows wind speed and wave height are the most prominent features in shoreline shift prediction. The model’s performance was compared, and the observed result suggests that the ANN model outperforms the KNN and SVM model with an accuracy of 86.2%.

Słowa kluczowe

EN

shoreline changes; image processing; artificial neural network; edge detection; machine learning

PL

zmiany linii brzegowej; przetwarzanie obrazu; sztuczna sieć neuronowa; wykrywanie krawędzi; nauczanie maszynowe

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2020
• Tom: Vol. 68, no. 4
• Strony: 1127--1143
• Opis fizyczny: Bibliogr. 112 poz.

Twórcy

autor

Kumar Lalit

• Department of Civil Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India

autor

Afzal Mohammad Saud

• Department of Civil Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India

autor

Afzal Mohammad Mashhood

• Birlasoft Limited, Tower 3 Assotech Business Cresterra Plot no 22, Noida-Greater Noida Expy, Sector 135, Noida, Uttar Pradesh, India

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)