Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

Meshram, Sarita Gajbhiye; Safari, Mir Jafar Sadegh; Khosravi, Khabat; Meshram, Chandrashekhar

doi:10.1007/s11356-020-11335-5

Cited by 37 publications

(8 citation statements)

References 77 publications

(82 reference statements)

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“…Traditionally, the sediment rating curve (SRC), which is a fitted relationship between suspended sediment concentration and river water discharge, has been utilised to assess trends and obtain predictions of SSLs, albeit having long response times and requiring a lot of information. However, a branch of artificial intelligence known as machine learning (ML), has been shown to effectively address these issues 5 while producing more accurate SSL predictions compared to SRCs 1 – 3 , 9 – 11 . ML and deep learning, which is a more specialized version of ML typically consisting of neural networks, have also been used to solve important prediction problems within various fields.…”

Section: Introductionmentioning

confidence: 99%

“…Other than the ANN and SVM, other algorithms have also been studied for the purpose of SSL prediction. Meshram et al 9 studied the iterative classifier optimizer-based pace regression (ICO-PR) and iterative classifier optimizer-based random forest (ICO-RF) for SSL prediction in the Seonath River basin, India. It was shown that the ICO-RF is more accurate than the ICO-PR, and stand-alone PR and RF models.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…Linked to the hydrological and environmental evolutionary modeling, there exists a significant progress in suspended sediment transport modeling in recent years. Understanding the sediment transport process and modeling such a complex phenomenon are of importance in water resources management [29,30]. The suspended sediment concentration (SSC) in the river is a crucial problem in environmental, hydraulic, and water resources engineering.…”

Section: Introductionmentioning

confidence: 99%

Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods

Samadianfard

Kargar

Shadkani

et al. 2021

Neural Comput & Applic

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Owing to the nonlinear and non-stationary nature of the suspended sediment transport in rivers, suspended sediment concentration (SSC) modeling is a challenging task in environmental engineering. Investigation of SSC is of paramount importance in river morphology and hydraulic structures operation. To this end, for SSC modeling, first random forest (RF) and multi-layer perceptron (MLP) standalone models were developed, and then, they were optimized with genetic algorithm (GA) and stochastic gradient descent (SGD) to develop GA-MLP, GA-RF, SGD-MLP, and SGD-RF hybrid models. Variety of input scenarios are implemented for SSC prediction to find the best input combination. The streamflow and SSC data collected from two stations of Minnesota and San Joaquin rivers, respectively, located at South Dakota and California are utilized in the current study. Accuracies of the developed models are examined by means of three performance criteria of correlation coefficient (CC), scattered index (SI), and Willmott's index of agreement (WI). A significant promotion in accuracy of hybrid models has been seen in contrast to their standalone counterparts. As can be deduced from the results, GA-MLP-5 and GA-RF-5 models with CC of 0.950 and 0.944, SI of 0.290 and 0.308, and WI of 0.974 and 0.971, respectively, were found as best models for prediction of SSC at Minnesota river. The developed SGD-MLP-5 and SGD-RF-5 models with CC of 0.900 and 0.901, SI of 0.339 and 0.339, and WI of 0.945 and 0.946, respectively, gave accurate results at San Joaquin river. Through the application of SGD algorithm, the adaptive learning rate, epochs, rho, L1 and L2 were activated and presumed as 0.004, 10, 1, 0.000009 and 0, respectively. The ExpRectifier was considered as san activation operation due to its better efficiency in comparison with its alternatives for predicting SSC in SGD-MLP model. According to the results, the fifth scenario that incorporates SSC t-1 , SSC t-2 , Q t , Q t-1 , and Q t-2 were found superior for SSC modeling in the studied rivers. The recommended hybrid algorithms based on GA and SGD optimization algorithms are proposed as practical tools for solving complex environmental problems.

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“…The major advantage of such SC techniques is that these models are fully nonparametric and do not require a priori concept of the relations between the input variables and the output data (Gocic ´et al 2015;Fahimi et al 2017). Various researchers have used ANNs for hydrologic studies including time series predictions of runoff or streamflow (Hsu et al 1995;Govindaraju 2000;Rajaee et al 2009Rajaee et al , 2010Melesse et al 2011;Lafdani et al 2013;Khan et al 2018;Meshram et al 2019aMeshram et al , 2019bMeshram et al , 2020Meshram et al , 2021aMeshram et al , 2021bMeshram et al , 2021cIraji et al 2020). Sudheer et al (2003) used radial-based neural networks for partial weather data; Trajkovic (2005) used radial-based neural networks using temperature-based models; Kisi (2007) applied a neural computing technique using climatic data; and Aytek (2008) applied a co-active neuro-fuzzy interpretation system.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer Perceptron (MLP)-Fire Fly Algorithm (FFA)-based model for sediment prediction

Meshram

Meshram²,

Pourhosseini

et al. 2021

Soft Comput

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The prediction of river sediment load is an essential issue in water resource engineering problems. In this study, artificial neural network employed in order to estimate the daily sediment load on rivers. Two different algorithms, multi-layer perceptron (MLP) and hybrid MLP-FFA (MLP integrated with the FFA) were used for this purpose in the Lake Mahabad, Iran. For this purpose, nine different scenarios are considered as inputs of the models. Performance of selected models was evaluated on basis of performance criterion namely root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R 2 ) for choosing best fit model. The results indicated that the new hybrid model MLP-FFA is successful in estimating sediment load with high accuracy as compared with its alternatives with RMSE = 2018 ton/day, MAE = 1698 and R 2 = 0.95, which were much lower than those of MLP-based model with RMSE = 3044 ton/day, MAE = 2481 and R 2 = 0.90. The results of the present study confirmed the suitability of proposed methodology for precise modeling of suspended sediment load.

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Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

Cited by 37 publications

References 77 publications

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

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

Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods

A Multi-Layer Perceptron (MLP)-Fire Fly Algorithm (FFA)-based model for sediment prediction

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