Regression models for sediment transport in tropical rivers

Harun, Mohd Afiq; Safari, Mir Jafar Sadegh; Gül, Enes; Ghani, Aminuddin Ab

doi:10.1007/s11356-021-14479-0

Cited by 16 publications

(5 citation statements)

References 58 publications

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“…Harun et al. (2021) developed six equations with two variable sets and three machine learning models. We present the two multi‐gene genetic‐programming (MGGP) equations as the representative models of the two variable sets.…”

Section: Dimensional Analysismentioning

confidence: 99%

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

Noh,

Park,

Seo

2023

Water Resources Research

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Sediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended‐to‐total load fraction using dimensionless hydro‐morphological variables. Two prominent variable combinations were identified using the recursive feature elimination for support vector regression (SVR): (1) width‐to‐depth ratio, dimensionless particle size, flow Reynolds number, densimetric Froude number, and falling particle Reynolds number, and (2) flow Reynolds number, Froude number, and densimetric Froude number. The explicit relations between the suspended‐to‐total load fraction and the two combinations were revealed by two modern symbolic regression methods: multi‐gene genetic programming and Operon. The five‐variable SVR model showed the best performance. Clustering analyses using a self‐organizing map and Gaussian mixture model, respectively, identified the underlying relationships between dimensionless variables. Subsequently, the one‐at‐a‐time sensitivity of the input variables of the empirical models was investigated. The suspended‐to‐total load fraction is positively related to the flow Reynolds number and is inversely related to the densimetric Froude number. The models developed in this study are practical and easy to implement in other suspended sediment monitoring methods because they only require basic measurable hydro‐morphological variables, such as velocity, depth, width, and median bed material size.This article is protected by copyright. All rights reserved.

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Section: Dimensional Analysismentioning

confidence: 99%

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

Noh,

Park,

Seo

2023

Water Resources Research

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“…It is a gauge of how well an estimated model's estimated values match up with actual data. [27], [28]:…”

Section: -Coefficient Of Determination (R2)mentioning

confidence: 99%

Models for Predicting River Suspended Sediment Load Using Machine Learning: A Survey

Chachan¹,

Bahnam²

2022

Technium

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Suspended sediment load (SSL) prediction study is critical to water resource management. This paper presents studies related to the prediction of SSL using machine learning (ML) algorithms over the last 13 years. This research gives a survey of current studies that are used machine learning techniques to predict sediment load on several rivers in different reign. Also, it aims to find a performance model to predict the SSL. This is done by making comparisons between several studies that used machine learning techniques to predict sediment load on several rivers using different time scales. Several metrics were used to determine the best prediction model. Most of the metrics used are: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared (R2) and Nash-Sutcliffe Efficiency Coefficient (NSE). The results of comparisons using different ML algorithms to predict the SSL have shown that the Multilayer perceptron (MLP) algorithm is the best compared to other algorithms.

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“…M5Tree is an alternative data-driven strategy that is extremely clear and does not necessitate the optimisation of network geometry and internal parameters 67 . M5 model has been used in the prediction of nonlinear hydrological parameters like piezometric head and seepage studies 68 , computation of missing rainfall data 69 , sediment transportation 70 , modelling of crop evapotranspiration 71 , establishing water level-discharge relationship 72 , rainfall-runoff modelling 73 . Another ML model used in the hydrological field is the reduced error pruning tree (REPTree), a decision tree model developed by Breiman 74 .…”

Section: Introductionmentioning

confidence: 99%

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

Swain,

Khura,

Sahoo

et al. 2024

Sci Rep

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An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio.

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Regression models for sediment transport in tropical rivers

Cited by 16 publications

References 58 publications

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers

Models for Predicting River Suspended Sediment Load Using Machine Learning: A Survey

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

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