Suspended sediment load prediction using non-dominated sorting genetic algorithm II

Tabatabaei, Mahmoud Reza; Jam, Amin Salehpour; Hosseini, S A

doi:10.1016/j.iswcr.2019.01.004

Cited by 17 publications

(7 citation statements)

References 37 publications

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“…The prediction performance is also visualized in this study using scatter plots, boxplots, normalized Taylor diagrams and heat maps. The scatter plot is the most common graph to make a direct comparison between predicted and observed outputs (Emamgholizadeh & Demneh, 2019, Hamaamin et al, 2019, Hassanpour et al, 2019, Sharghi et al, 2019, Tabatabaei et al, 2019. A normalized Taylor diagram plots together R, RMSE and normalized standard deviation; the closer the model performance point in the diagram to the "observed" point (RMSE = 0, R = 1, normalized standard deviation = 1), the better the performance of the model is (Taylor, 2001).…”

Section: [Table 5]mentioning

confidence: 99%

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Sharafati

Asadollah

Motta

et al. 2020

Hydrological Sciences Journal

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Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely Gradient Boost Regression (GBR), Ada Boost Regression (ABR) and Random Forest Regression (RFR) are proposed for prediction of Suspended Sediment Load (SSL), and their prediction performance and related uncertainty are assessed. The Suspended Sediment Load (SSL) of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river Discharge (Q) and Suspended Sediment Concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.

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Section: [Table 5]mentioning

confidence: 99%

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Sharafati

Asadollah

Motta

et al. 2020

Hydrological Sciences Journal

View full text Add to dashboard Cite

show abstract

“…Four performance measures are selected to evaluate the models’ performances, namely the mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R 2 ), and ranking mean (RM). MAE, RMSE, and R 2 have been commonly used in SSL prediction studies 2 , 8 – 10 , 19 , 21 , 22 , 24 , 37 , 38 , 69 , while RM was used by Ahmed et al 70 as a method to rank overall model performance.…”

Section: Methodsmentioning

confidence: 99%

“…The study showed bagging-M5P to be superior to the classical M5P, reduced error pruning tree (REPT), instance-based learning (IBK), and hybridized versions of the REPT model. Tabatabaei et al 10 predicted SSL using data from the Ramian hydrological station on the Ghorichay River, Iran by utilizing an SRC model optimized with the non-dominated sorting genetic algorithm II (NSGA-II), which increased prediction efficiency. In the study by Uca et al 4 , multiple linear regression (MLR) and ANN were tested to predict SSL for the Jenderam catchment, Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

Essam

Huang

Birima

et al. 2022

Sci Rep

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High loads of suspended sediments in rivers are known to cause detrimental effects to potable water sources, river water quality, irrigation activities, and dam or reservoir operations. For this reason, the study of suspended sediment load (SSL) prediction is important for monitoring and damage mitigation purposes. The present study tests and develops machine learning (ML) models, based on the support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM) algorithms, to predict SSL based on 11 different river data sets comprising of streamflow (SF) and SSL data obtained from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a single model that is capable of accurately predicting SSLs for any river data set within Peninsular Malaysia. The ANN3 model, based on the ANN algorithm and input scenario 3 (inputs consisting of current-day SF, previous-day SF, and previous-day SSL), is determined as the best model in the present study as it produced the best predictive performance for 5 out of 11 of the tested data sets and obtained the highest average RM with a score of 2.64 when compared to the other tested models, indicating that it has the highest reliability to produce relatively high-accuracy SSL predictions for different data sets. Therefore, the ANN3 model is proposed as a universal model for the prediction of SSL within Peninsular Malaysia.

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“…e study of MOEA/D has been a hot topic lately [1]. It has proved to be amongst the highly competitive and dominating Multiobjective Evolutionary Algorithms (MOEAs) alongside other algorithms such as Multiobjective Differential Evolution (MODE) [2], Multiobjective Particle Swarm Optimization (MOPSO) [3], and Nondominated Sorting Genetic Algorithm-II (NSGA-II) [4]. Like other evolutionary algorithms (EAs), MOEA/D is also a populationbased MOEA, and it is inspired by a decomposition mechanism that decomposes a multiobjective problem (MOP) into a set of single-objective optimization subproblems that are equivalent to the population of candidate solutions (on other MOEAs), where each subproblem is assigned with a unique weight vector [1].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multiobjective Optimization Based on Nondominated Sorting and Crowding Distance, with Applications to Wave Energy Converters

Saveca

Sun

Wang

2022

International Transactions on Electrical Energy Systems

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Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously based on the MOP neighborhood information. However, the MOEA/D strategy tends to produce a distributed optimization that is not of good quality in some problems with complex Pareto optimal front, such as problems with a long tail and sharp peak, common in real-world situations. This paper proposes an improved MOEA/D to enhance the distributed optimization quality and minimize its complexity while accelerating the optimization to get a better solution. The improved method is achieved by incorporating a Hybrid Differential Evolution/Particle Swarm Optimization algorithm and a hybrid operator based on nondominated sorting and crowding distance algorithm. This incorporation takes place in the mutation generator and initial population part of the original MOEA/D algorithm. Simulations and comparisons are carried out based on some MOP benchmark functions to verify the proposed method’s performance. The experimental results show that the proposed method achieves better performance compared to other algorithms. Furthermore, the proposed method is also applied to optimize the multiobjective wave energy converter model to maximize power per year and minimize cost per unit power.

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Suspended sediment load prediction using non-dominated sorting genetic algorithm II

Cited by 17 publications

References 37 publications

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

A Hybrid Multiobjective Optimization Based on Nondominated Sorting and Crowding Distance, with Applications to Wave Energy Converters

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