Forecasting river sediment deposition through satellite image driven unsupervised machine learning techniques

Ahmed, Nizam; Mahmud, Safin; Elahi, M. M. Lutfe; Ahmed, Silvia; Sujauddin, Mohammad

doi:10.1016/j.rsase.2018.12.011

Cited by 7 publications

(4 citation statements)

References 63 publications

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“…The methods used for machine learning and statistical modeling (Tan et al., 2021; Yu et al., 2021) implemented here have previously been described (Ahmed et al., 2019; Recaldes et al., 2020; Svatos, 2021). The theory behind the installation and configuration of such implementations for environmental simulation has also previously been described (Svatos, 2018).…”

Section: Methodsmentioning

confidence: 99%

Heuristics‐enhanced geospatial machine learning (SaaS) of an ancient Mediterranean environment

Svatos

Ahmed

Recaldes

et al. 2022

Soil Science Soc of Amer J

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Raw soil core physical data used in machine learning algorithms with corresponding spatial remotely sensed data is an emerging science. Using data derived from soil core samples previously collected in Universal Transverse Mercator zone 50 (Western Australia) and remotely sensed data, a model that predicted ground movement (GM) was developed specific to Australian Standards manual AS 1726-2017. This is the first approach for Australian soils and first in the world for soils older than 200 million yr. The model developed reliably predicted GM with 91.1% accuracy. The error obtained from the prediction is within acceptable limits currently used by engineers in calculations concerning soil classification for engineering purposes. Concerning the remotely sensed data analyzed, accuracy of the Atterberg limits method might be improved if additional information about soil structure (layering and horizon) or other variables (seasonal data) are built into this model. This model can be used to save on construction material costs, reduce the potential for human error associated with data collection and sample manipulation, but also fast-track (by up to 6 wk based on current wait times) building approvals while ensuring compliance to the relevant legislation. This platform also reduces the environmental effects of invasive drilling techniques. A requirement within principles of sustainable building practices, and associated with current standards commonly used by structural engineers who may seek better understanding of soil properties in Australia as a software service (with application potential in North America).

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Section: Methodsmentioning

confidence: 99%

Heuristics‐enhanced geospatial machine learning (SaaS) of an ancient Mediterranean environment

Svatos

Ahmed

Recaldes

et al. 2022

Soil Science Soc of Amer J

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“…For example, Xu et al (2019a) used the concept of geological landform regions to verify the clustering results of sedimentation potential from a self-organizing map. Ahmed et al, 2018;Xu et al, 2019a Urban infrastructure (section 2.4.1)…”

Section: Annmentioning

confidence: 99%

“…(a) Random forests (Francke et al, 2008;López-Tarazón et al, 2012), (b) Genetic algorithms (Altunkaynak, 2009;Yadav et al, 2019b), (c) Unsupervised techniques (Ahmed et al, 2018;Xu et al, 2019a).…”

Section: Predicting Sediment Loadmentioning

confidence: 99%

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

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Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.

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“…Aziz et al [20] used the unsupervised machine-learning algorithms, such as the k-means, clustering large application, and hierarchical agglomerative clustering for predicting river-sediment adaptation. Ahmed et al [21] used the same methods as used by Aziz et al [20] as well as the self-organizing tree algorithm to perform supervised classification for satellite images and predict future river-sedimentation areas. However, there are some limitations for these machine-learning methods.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

et al. 2022

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The Tongtian River is the source of the Yangtze River and is a national key ecological reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian River channel morphology are beneficial to protecting the “Asian Water Tower”. This study aims to quantitatively monitor and predict the accretion and erosion area of the Tongtian River channel morphology during the past 30 years (1990–2020). Firstly, the water bodies of the Tongtian River were extracted and the accretion and erosion areas were quantified using 1108 Landsat images based on the combined method of three water-body indices and a threshold, and the surface-water dataset provided by the European Commission Joint Research Centre. Secondly, an intelligent lightweight neural-network model was constructed to predict and analyze the accretion and erosion area of the Tongtian River. Results indicate that the Tongtian River experienced apparent accretion and erosion with a total area of 98.3 and 94.9 km2, respectively, during 1990–2020. The braided (meandering) reaches at the upper (lower) Tongtian River exhibit an overall trend of accretion (erosion). The Tongtian River channel morphology was determined by the synergistic effect of sediment-transport velocity and streamflow. The lightweight neural network well-reproduced the complex nonlinear processes in the river-channel morphology with a final prediction error of 0.0048 km2 for the training session and 4.6 km2 for the test session. Results in this study provide more effective, reasonable, and scientific decision-making aids for monitoring, protecting, understanding, and mining the evolution characteristics of rivers, especially the complex change processes of braided river channels in alpine regions and developing countries.

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Forecasting river sediment deposition through satellite image driven unsupervised machine learning techniques

Cited by 7 publications

References 63 publications

Heuristics‐enhanced geospatial machine learning (SaaS) of an ancient Mediterranean environment

Heuristics‐enhanced geospatial machine learning (SaaS) of an ancient Mediterranean environment

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Monitoring and Predicting Channel Morphology of the Tongtian River, Headwater of the Yangtze River Using Landsat Images and Lightweight Neural Network

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