Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms

Gül, Enes; Safari, Mir Jafar Sadegh; Haghighi, Ali Torabi; Mehr, Ali Danandeh

doi:10.1371/journal.pone.0258125

Cited by 7 publications

(1 citation statement)

References 36 publications

(60 reference statements)

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“…In such cases, time series modeling and artificial intelligence models might be combined to account for hydrological processes rather than utilizing a single model [10]. It is well recognized that the experimental dataset, the machine learning model, and the use of efficient variables for model creation depending on such a challenge are all very important components in building a reliable machine learning technique [11].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

et al. 2022

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Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance.

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Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

et al. 2022

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Lq-norm multiple kernel fusion regression for self-cleansing sediment transport

Safari,

Rahimzadeh Arashloo,

Kohandel Gargari

2024

Artif Intell Rev

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Experimental and modeling studies have been conducted to develop an approach for self-cleansing rigid boundary open channel design such as drainage and sewer systems. Self-cleansing experiments in the literature are mostly performed on circular channel cross-section, while a few studies considered self-cleansing sediment transport in small rectangular channels. Experiments in this study were carried out in a rectangular channel with a length of 12.5 m, a width of 0.6 m, a depth of 0.7 m and having an automatic control system for regulating channel slope, discharge and sediment rate. Behind utilizing collected experimental data in this study, existing data in the literature for rectangular channels are used to develop self-cleansing models applicable for channel design. Through the modeling procedure, this study recommends Lq-norm multiple kernel fusion regression (LMKFR) techniques for self-cleansing sediment transport. The LMKFR is a regression technique based on the regularized kernel regression method which benefits from the combination of multiple information sources to improve the performance using the Lq-norm multiple kernel learning framework. The results obtained by LMKFR are compared to support vector regression benchmark and existing conventional regression self-cleansing sediment transport models in the literature for rectangular channels. The superiority of LMKFR is illustrated in an accurate modeling as compared with its alternatives in terms of various statistical error measurement criteria. The encouraging results of LMKFR can be linked to utilization of several kernels which are fused effectively using an Lq-norm prior that captures the intrinsic sparsity of the problem at hand. Promising performance of LMKFR technique in this study suggests it as an effective technique to be examined in similar environmental, hydrological and hydraulic problems.

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Topological graph representation of stratigraphic properties of spatial-geological characteristics and compression modulus prediction by mechanism-driven learning

Wang

Liu

et al. 2023

Computers and Geotechnics

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Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms

Cited by 7 publications

References 36 publications

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

Lq-norm multiple kernel fusion regression for self-cleansing sediment transport

Topological graph representation of stratigraphic properties of spatial-geological characteristics and compression modulus prediction by mechanism-driven learning

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