Recent views on the origin of river meandering and braiding

Semantic image classification as practised in Earth Observation is poorly suited to mapping fluvial landforms which are often composed of multiple landcover types such as water, riparian vegetation and exposed sediment. Deep learning methods developed in the field of computer vision for the purpose of image classification (ie the attribution of a single label to an image such as cat/dog/etc) are in fact more suited to such landform mapping tasks. Notably, Convolutional Neural Networks (CNN) have excelled at the task of labelling images. However, CNN are notorious for requiring very large training sets that are laborious and costly to assemble. Similarity learning is a sub-field of deep learning and is better known for one-shot and few-shot learning methods. These approaches aim to reduce the need for large training sets by using CNN architectures to compare a single, or few, known examples of an instance to a new image and determining if the new image is similar to the provided examples. Similarity learning rests on the concept of image embeddings which are condensed higher-dimension vector representations of an image generated by a CNN. Ideally, and if a CNN is suitably trained, image embeddings will form clusters according to image classes, even if some of these classes were never used in the initial CNN training.&#160;In this paper, we use similarity learning for the purpose of fluvial landform mapping from Sentinel-2 imagery. We use the True Color Image product with a spatial resolution of 10 meters and begin by manually extracting tiles of 128x128 pixels for 4 classes: non-river, meandering reaches, anastomosing reaches and braiding reaches. We use the DenseNet121 CNN topped with a densely connected layer of 8 nodes which will produce embeddings as 8-dimension vectors. We then train this network with only 3 classes (non-river, meandering and anastomosing) using a categorical cross-entropy loss function. Our first result is that when applied to our image tiles, the embeddings produced by the trained CNN deliver 4 clusters. Despite not being used in the network training, the braiding river reach tiles have produced embeddings that form a distinct cluster. We then use this CNN to perform few-shot learning with a Siamese triplet architecture that will classify a new tile based on only 3 examples of each class. Here we find that tiles from the non-river, meandering and anastomising class were classified with F1 scores of 72%, 87% and 84%, respectively. The braiding river tiles were classified to an F1 score of 80%. Whilst these performances are lesser than the 90%+ performances expected from conventional CNN, the prediction of a new class of objects (braiding reaches) with only 3 samples to 80% F1 is unprecedented in river remote sensing. We will conclude the paper by extending the method to mapping fluvial landforms on entire Sentinel-2 tiles and we will show how we can use advanced cluster analyses of image embeddings to identify landform classes in an image without making a priori decisions on the classes that are present in the image.

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DIMENSIONAL ANALYSIS RELATIONSHIPS OF STREAMBANK EROSION RATES

Jurnal Teknologi ◽

10.11113/jt.v78.8580 ◽

2016 ◽

Vol 78 (5-5) ◽

Author(s):

Azlinda Saadon ◽

Junaidah Ariffin ◽

Jazuri Abdullah ◽

Norhidayati Mat Daud

Keyword(s):

Dimensional Analysis ◽

Physical Characteristics ◽

Bank Failure ◽

Erosion Process ◽

Streambank Erosion ◽

Fluvial Erosion ◽

Erosion Rates ◽

Erosion Processes ◽

Hydraulic Force ◽

River Meandering

Bank erosion is commonly associated with river meandering initiation and development, through width adjustment and planform evolution. It consists of two types of erosion processes; basal erosion due to fluvial hydraulic force and bank failure under the influence of gravity. Most of the studies only focused on one factor rather than integrating both factors. Evidences of previous works have shown integration between both processes of fluvial hydraulic force and bank failure. Bank failure seldom treated as a probabilistic phenomenon without assessing the physical characteristics and the geotechnical aspects of the bank. Thus, the objective of this paper is to investigate factors governing streambank erosion process and to perform a dimensional analysis considering the physical characteristics of both processes namely fluvial erosion and mass failure and their interaction.

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River Meandering as a Self-Organization Process

Science ◽

10.1126/science.271.5256.1710 ◽

1996 ◽

Vol 271 (5256) ◽

pp. 1710-1713 ◽

Cited By ~ 162

Author(s):

H.-H. Stolum

Keyword(s):

Self Organization ◽

Organization Process ◽

River Meandering

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Modeling of discharge fluctuation influence on river meandering geometry change

International Journal of Academic Research ◽

10.7813/2075-4124.2012/4-6/a.26 ◽

2012 ◽

Vol 4 (6) ◽

pp. 189-196 ◽

Cited By ~ 2

Author(s):

Kuntjoro ◽

Mohammad Bisri ◽

Aniek Masrevaniah ◽

Agus Suharyanto

Keyword(s):

River Meandering

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River meandering dynamics

Physical Review E ◽

10.1103/physreve.65.046303 ◽

2002 ◽

Vol 65 (4) ◽

Cited By ~ 43

Author(s):

Boyd F. Edwards ◽

Duane H. Smith

Keyword(s):

River Meandering

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Applying micro-genetic algorithm in the one-dimensional unsteady hydraulic model for parameter optimization

Journal of Hydroinformatics ◽

10.2166/hydro.2013.030 ◽

2013 ◽

Vol 16 (4) ◽

pp. 772-783 ◽

Cited By ~ 3

Author(s):

Tsun-Hua Yang ◽

Yu-Chi Wang ◽

Shun-Chung Tsung ◽

Wen-Dar Guo

Keyword(s):

Evaluation Criteria ◽

Hydraulic Modeling ◽

Hydraulic Model ◽

Roughness Coefficient ◽

Limited Information ◽

Good Tool ◽

Physical Conditions ◽

The One ◽

Flow Quantity ◽

River Meandering

Selection of an appropriate value for Manning's roughness coefficient could significantly impact the accuracy of a hydraulic model. However, it is highly variable and depends on flow circumstances, such as water stage and flow quantity; a stream's geomorphology, such as the fluvial process and river meandering; and physical conditions, such as the channel surface roughness and irregularities. Nevertheless, choosing proper roughness coefficients is not easy, especially with limited information and time in a practical application. Even it is done for a specific event it may not apply to another event due to its time- and site-dependency. This study proposes a Visual Basic (VB)-based system, which integrates the HEC-RAS modeling tool and the μGA to efficiently search for Manning's roughness coefficients. The matching coefficients will thereafter improve the accuracy of hydraulic modeling. Two events in the Yilan River Basin were applied to test the feasibility of the system and four evaluation criteria were used to evaluate the system performance. The results showed that μGA efficiently converged and the hydraulic model showed good agreement in comparison with the measured data. The system can be used as a good tool for finding onsite Manning's roughness coefficients in hydraulic modeling when detailed information is not available.

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