Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

Tao, Hai; Al-Khafaji, Zainab S.; Qi, Chongchong; Zounemat‐Kermani, Mohammad; Kisi, Özgür; Tiyasha, Tiyasha; Chau, Kwok Wing; Nourani, Vahid; Melesse, Assefa M.; Elhakeem, Mohamed; Farooque, Aitazaz A.; Nejadhashemi, A. Pouyan; Khedher, Khaled Mohamed; Alawi, Omer A.; Deo, Ravinesh C.; Shahid, Shamsuddin; Singh, Vijay P.; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1080/19942060.2021.1984992

Cited by 28 publications

(13 citation statements)

References 180 publications

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“…Similar to sedimentation, dredging has numerous environmental effects. In addition, open-water sediment disposal is linked to numerous coastal and offshore locations worldwide, including land reclamation, coastline expansion, dredging, and the isolation of polluting sediments (Reisenbüchler et al 2021;Sharafati et al 2020;Tao et al 2021). The impact on the marine environment is one of the most important aspects of dredging and resulting materials from ports and waterways.…”

Section: Innovations and Research Objectivesmentioning

confidence: 99%

The impacts of dumping sites on the marine environment: a system dynamics approach

2023

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The various forms of anthropogenic pollution of seas and oceans have been extensively studied in recent decades. The most significant factors are the destructive environmental impacts of marine dumping sites. These sites put soil and coastline, water quality, mangroves and coral reefs, marine animals, food chains and plankton, and fishery at serious risk and alter the surrounding economic, social, and cultural conditions. The destruction of marine ecosystems by dumping sites causes severe environmental damage. With the ever-increasing anthropogenic environmental pollution of the seas and the drastic reduction in the self-purification mechanism of marine ecosystems, it is necessary to charter practical solutions with a holistic perspective and implement novel designs using system dynamics. System thinking and system analysis are essential tools in analyzing and solving important economic and management issues. System analysis investigates and evaluates the system complexities, determines the relationship between all factors, and then presents appropriate solutions to the problem. The increasing severity of the problems caused by marine dumping in recent years and the existing research gap in this area have highlighted the need for effective, comprehensive, and integrated solutions. The process of findings such solutions is critical and challenging. This study explores the most significant factors that directly and indirectly impact marine life using system dynamics.

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Section: Innovations and Research Objectivesmentioning

confidence: 99%

The impacts of dumping sites on the marine environment: a system dynamics approach

2023

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“…But the Artificial neural network is a black-box process, which cannot observe the intermediate results, and the learning process is relatively long and may fall into a local minimum [46], [47]. The SVM has little explanatory power for high-dimensional mapping of kernel functions, especially radial basis functions [48], [49]. Therefore, this article first considers the flexibility and robustness of the decision tree model (boosting/bagging), which has distinct advantages in parameter optimization and interpretation analysis [50], [51].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Planetscope Imagery and Machine Learning for Estimating Suspended Particulate Matter in the Ebinur Lake, Xinjiang, China

Duan

Fei

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Ebinur Lake is a shallow lake and vulnerable to strong winds, which can lead to drastic changes in suspended particulate matter (SPM). High spatial and temporal resolution images are therefore urgently needed for SPM monitoring over the Ebinur Lake. Hence, a high-efficiency inversion model of estimating SPM from high-resolution images using machine learning is essential to increase the amount of extracted information through band combinations quadratic optimization. This article aims to evaluate the capability of the PlanetScope images and four machine learning approaches for estimating SPM of the Ebinur Lake. The specific objectives include: to obtain the sensitive bands and band combinations for SPM using correlation analysis; to quadratically optimize the combination pattern of sensitive bands using a linear model; and to compare the accuracy of traditional linear model and machine learning models in estimating SPM. The results of the study confirm that after linear model quadratic optimization, the band combinations of B3 * B4, (B2+B3)/ (B2-B3), (B3+B4) * (B3+B4), and (B3-B2)/(B2/B3) have higher accuracy than that of the single band model. By inputting the preferred four-band combinations into the partial least squares, random forest, extreme gradient boosting, gradient boosting decision tree, and categorical boosting (CatBoost) models, the performance of the SPM inversion based on PlanetScope images is better than the traditional linear model. Validation of the inversion maps with observations further indicates that the CatBoost model performed the best.

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“…The hybrid Particle Swarm Optimization and Genetic Algorithm (PSO-GA) performed best among other hybrid machine learning models [28]. Another study conducted a literature review of publications focused on the application of artificial intelligence models to river sedimentation and concluded that the main limitations of AI models are their low applicability to other watersheds which have dissimilar morphological and climatic characteristics, and their lack of physical interpretation [29].…”

Section: Introductionmentioning

confidence: 99%

A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea

Kareem

Seong

Kim

et al. 2022

Water

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Monitoring tidal dynamics is imperative to disaster management because it requires a high level of precision to avert possible dangers. Good knowledge of the physical drivers of tides is vital to achieving such a precision. The Taehwa River in Ulsan City, Korea experiences tidal currents in the estuary that drains into the East Sea. The contribution of wind to tide prediction is evaluated by comparing tidal predictions using harmonic analysis and three deep learning models. Harmonic analysis is conducted on hourly water level data from 2010–2021 using the commercial pytides toolbox to generate constituents and predict tidal elevations. Three deep learning models of long short-term memory (LSTM), gated recurrent unit (GRU), and bi-directional lstm (BiLSTM) are fitted to the water level and wind speed to evaluate wind and no-wind scenarios. Results show that Taehwa tides are categorized as semidiurnal tides based on a computed form ratio of 0.2714 in a 24-h tidal cycle. The highest tidal range of 0.60 m is recorded on full moon spring tide indicating the significant lunar pull. Wind effect improved tidal prediction NSE of optimal LSTM model from 0.67 to 0.90. Knowledge of contributing effect of wind will inform flood protection measures to enhance disaster preparedness.

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Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

Cited by 28 publications

References 180 publications

The impacts of dumping sites on the marine environment: a system dynamics approach

The impacts of dumping sites on the marine environment: a system dynamics approach

High-Resolution Planetscope Imagery and Machine Learning for Estimating Suspended Particulate Matter in the Ebinur Lake, Xinjiang, China

A Case Study of Tidal Analysis Using Theory-Based Artificial Intelligence Techniques for Disaster Management in Taehwa River, South Korea

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