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

Kumar Lalit, Afzal Mohammad Saud, Afzal Mohammad Mashhood

Acta Geophysica

2020

Vol. 68, no. 4

1127--1143

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%.

Wykorzystanie historycznych zdjęć lotniczych do analizy zmian brzegu na przykładzie fragmentu wybrzeża Zatoki Pomorskiej

Dudzińska-Nowak J., Furmańczyk K.

Roczniki Geomatyki

2005

T. 3, z. 4

53-61

The knowledge about long- and short-term coastal changes plays a key role in Integrated Coastal Zone Management processes. This project was realized in the Laboratory of Remote Sensing and Marine Cartography of the University of Szczecin, within the framework of .Remote sensing research of the tendency the coastline changes of the Pomeranian Bay (3P04E05023). financed from the State Committee for Scientic Reseaerch (KBN) resources. The following data were used in this project: historical aerial photographs taken in 1938, 1951, 1973, 1996, topographic maps in the scale 1:10000, technical belt maps in the scale 1:2000 and DEM created for 2 km wide coastal belt. The area of research is 100 km long and is located at the Eastern part of Pomeranian Bay from Swinoujscie to Kolobrzeg. On the basis of the aerial photographs taken in 1996 with the use of DEM and OrthoMaster software an orthophotomap was created. The others aerial photographs taken in 1938, 1951 and 1973 were calibrated on the basis of this orthophotomap. Finally, all aerial photographs and maps were transfered into the same coordinate system PUWG1992/19. A dune base line / cliff food line and the geodetic monuments (kilometrage of the coast) were identified on every picture. Changes of dune base line / cliff food line location in time were measured. Results were presented for three selected areas of the coast located near Swina, Dziwna and Rega river mouths. The analysis of the coastal changes in the river mouth areas shows that this sections of the coast are very dynamic and have a big variability of the morphodynamic processes. This research can be useful in the protection of the coast, indicating relatively stable or dynamic places along the coast. The knowledge about such places is very important in the aspect of erosion hazard and safe planning in the Integrated Coastal Zone Management.