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

Kumar, Lalit; Afzal, Mohammad Saud; Afzal, Mohammad Mashhood

doi:10.1007/s11600-020-00454-9

Cited by 29 publications

(11 citation statements)

References 75 publications

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“…Investigating decadal shoreline change is essential to understand the influence of coastal processes [62,63]. To monitor erosion-accretion patterns of the YRD, the shoreline dynamics and land area changes were extracted and monitored by Landsat satellite data.…”

Section: Shoreline Extraction Of the Yrdmentioning

confidence: 99%

Damming-Induced Hydrogeomorphic Transition in Downstream Channel and Delta: A Case Study of the Yellow River, China

Chen

Jiang

et al. 2022

Water

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River dynamics and sediment budget play a crucial role in shaping geomorphic variability of river channels and deltaic environments. Basin-scale human activities, including dam construction, induce alterations in river flow and sediment dynamics in the downstream channels and to the delta, and quantification of sediment source shift along downstream fluvial-deltaic systems is often uncertain. This study analyzed the river regime changes and sediment dynamics of a typical sediment-laden fluvial-deltaic system—the lower Yellow River (LYR) and the Yellow River Delta (YRD) —to assess the integrated effects of dam impoundment and dam-based river regulation schemes on downstream hydrogeomorphic transition processes. The Xiaolangdi (XLD) Reservoir, which was completed in 2000 with a total storage of 12.7 km3, is the final reservoir located in the middle Yellow River and plays an important role in flood control and energy supply. Following the full operation of XLD Reservoir, the relationship between water and sediment in the LYR became more balanced, with a drastic decline of sediment input and seasonal migrations of floodwaters. The interannual variability of water levels at downstream hydrological stations indicated a geomorphic transition in the LYR from net deposition to erosion state. The building of the XLD Reservoir caused a downstream shift of river-originated sediment source and 48% of the total sediment delivered to the YRD was derived from the LYR. However, the reduced sediment delivery since 2000 has still triggered net land loss regarding the YRD system, with a strong spatial variability which is dominated by the reduced accretion at the active delta front and erosion at the abandoned river mouth and coastal engineering zone. Compared with other environmental factors, the construction of upstream dams contributed the most to the decline of downstream sediment delivery over the past decades. The challenge for sustainable sediment management is the gradual decline of scouring efficiency as the riverbed sediment is coarsening. Our study suggests that future river regulation strategies should consider the geomorphic sustainability of both the LYR and the YRD system.

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Section: Shoreline Extraction Of the Yrdmentioning

confidence: 99%

Damming-Induced Hydrogeomorphic Transition in Downstream Channel and Delta: A Case Study of the Yellow River, China

Chen

Jiang

et al. 2022

Water

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“…TP FP FN = + + (14) Makale kapsamında kıyı çizgisinin çıkarımı, yapılan çalışmalar ile karşılaştırıldığında Kumar vd., (2020), Hindistan Odissa kıyısı için K-En yakın komşu (KNN), DVM, yapay sinir ağları yöntemleri kullanarak yaptıkları çalışmada %80-%84 arasında bir doğruluk elde etmişlerdir [22]. Makale kapsamında kıyı çizgisinin belirlenmesi için üretilen doğruluk %14-%18 arasında bir artış sağlamıştır.…”

Section: Tp Iouunclassified

Machine Learning Based Segmentation of Shoreline Using Mean-Shift, Random Forest and Support Vector Machine

Akca

Şanlı

2022

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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The coast is an area that forms the boundary between land and sea and contains a certain ecosystem of life. To determine the coastline where sea areas are separated from the land in climatic events such as storms, tornadoes, hurricanes, which occur due to the withdrawal or rise of waters due to climate change, tidal movements, weather events in tropical ecosystems. In this study, machine learning-based mean-shift, random forest (RO), and support vector machines (SVM) methods were used on the Sentinel-2A satellite image, and the results were evaluated by calculating overall accuracy and using IOU metrics. The overall accuracies are 97.23%, 99.15%, and 98.68% for mean-shift, random forest, and support vector machines, respectively.

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“…In 2020, binary particle swarm optimization based edge detection methodology minimizing multi-objective fitness function is proposed by NSD [27], the algorithms have a great applicability in microscopic measurements. The Sobel operator was used in satellite images by Kumar et al [28] to monitor shoreline boundaries regularly. Lu et al [29] proposed a human flexibility test via image processing, and the Canny detection operator was adopted for feature point extraction.…”

Section: Related Workmentioning

confidence: 99%

Parameter detection for surface acoustic wave filter based on image processing

Liu¹,

Li²

2021

Meas. Sci. Technol.

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As core devices for the smartphone RF front end, surface acoustic wave (SAW) filters are in short supply. The detection of SAW filter parameters refers to the connection between layout design and process production, which is an important part of capacity improvement. This paper devises a method based on image processing to detect SAW filter parameters. We improve the morphological operator to recover an image and identify the number of interdigital transducer (IDT) finger bars under noise conditions. The feature points in the color image are selected to extract the scale. The Canny edge detection effect is enhanced through using an adaptive filter, increasing the gradient operator, bilinear interpolation, and other algorithms. We use a minimum shape to enclose the edges to determine the IDT width and period and apply Fourier transform to verify the detection results. The experimental results show that the number of IDT finger bars is accurately identified. The width and period are exactly the same as the real values at the 10th percentile, and the detection requirements are met.

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

Cited by 29 publications

References 75 publications

Damming-Induced Hydrogeomorphic Transition in Downstream Channel and Delta: A Case Study of the Yellow River, China

Damming-Induced Hydrogeomorphic Transition in Downstream Channel and Delta: A Case Study of the Yellow River, China

Machine Learning Based Segmentation of Shoreline Using Mean-Shift, Random Forest and Support Vector Machine

Parameter detection for surface acoustic wave filter based on image processing

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