A new approach to predict the missing values of algae during water quality monitoring programs based on a hybrid moth search algorithm and the random vector functional link network

Hussein, Ahmed; Elaziz, Mohamed Abd; Wahed, Mahmoud S. M. Abdel; Sillanpää, Mika

doi:10.1016/j.jhydrol.2019.05.073

Cited by 27 publications

(10 citation statements)

References 62 publications

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“…Here, the ML-KRVFL model performs better than other compared models. Forecasting power consumption and water productivity of seawater Sharshir et al [111] Firefly algorithm based RVFL (FA-RVFL) Sigmoid, radbas, hardlim, sine, sign, tribas Firefly algorithm Thermal performance and modeling prediction of developed pyramid solar Pan et al [55] Jaya-RVFL -Jaya algorithm Transient stability assessment of power systems Parija et al [74] Empirical mode decomposition (EMD) and water cycle algorithm (WCA) based weighted multikernel RVFL network (WMKRVFLN) (WCA-EMD-WMKRVFLN) -Closed form (LS) Epileptic EEG signal classification Hussein et al [112] Moth search algorithm based RVFL (MSA-RVFL) -Closed form (LS) Water quality analysis Zhang et al [29] Sparse auto encoder with l 1 norm based RVFL (SP-RVFL)…”

Section: Multi-label Classification Based On Rvfl Modelmentioning

confidence: 99%

“…Besides the above popular areas with a large need for forecasting, RVFL and its improved versions have also succeeded in various areas, such as temperature [131], landslide displacement [182], COVID-19 cases [108], travel time [146], molten iron quality [118,117], energy consumption [147], signal-to-noise ratio [185], algae missing values [112], temperature in subway station [186] and so on [176].…”

Section: Other Datamentioning

confidence: 99%

“…Among these literature, many utilize different heuristic algorithms to optimize the hyper-parameters [110,111,109,80], weights [187] or select the input features [112]. For instance, firefly algorithm is utilized to select RVFL's hyperparameters, number of enhancement nodes, bias, direct link, distribution and activation function, for thermal performance prediction [108].…”

Section: Other Datamentioning

confidence: 99%

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Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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Neural networks have been successfully employed in various domain such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. RVFL model has several characteristics such as fast training speed, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the comprehensive review of the development of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and ensemble deep RVFL models. The variations, improvements and applications of RVFL models are discussed in detail. Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. Finally, we give potential future research directions/opportunities that can inspire the researchers to improve the RVFL architecture further.

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Section: Multi-label Classification Based On Rvfl Modelmentioning

confidence: 99%

Section: Other Datamentioning

confidence: 99%

Section: Other Datamentioning

confidence: 99%

See 1 more Smart Citation

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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“…There are numerous continuous studies on algae ecology including algal classification, algal detection, water quality profiling, algal analysis, and algal prediction [31], [32], [33], [34], [35]. Nevertheless, this paper focuses on both WQMS and algal prediction model development.…”

Section: Related Workmentioning

confidence: 99%

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

et al. 2021

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An end-to-end process to achieve a complete framework methodology for Harmful Algal Bloom (HAB) growth prediction is crucial for water management, especially in implementing robust predictive modelling of HAB to prevent water pollution. Previous works have separately focused on the prediction part or the implementation of the water monitoring system that involves the integration of sensors through the Internet of Things (IoT). These studies lack in terms of discussion of both IoT with the algae ecological domain and prediction method. Therefore, this paper takes the initiative to provide a wider coverage on the end-to-end process including the assembly and integration of sensors, data acquisition and predictive modelling using data-driven approaches, for example, machine learning, deep learning and deep time series forecasting algorithm for future algal bloom outbreak mitigation. This paper believes that discussion in a complete framework perspective based on the execution of each phase is important besides providing a true understanding of the algae growth factors and prediction problems to achieve a robust prediction algorithm for algal growth. In the end, this paper presents proof that selecting the right features and utilising time series with deep learning are much better for tackling the issues of highly non-linear and dynamic algae ecological data that are briefly introduced in this paper. Among all the algorithms selected, Long Short-term Memory (LSTM) is the best fit for the prediction method and has outperformed other basic machine learning methods in accurately predicting algal growth through the prediction of chlorophyll-a (Chla) as a strong indicator of algal presence for coastal studies.

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“…It is noted that the process of fine-tuning a machine learning model can be formulated as a global optimization problem. In recent years, various metaheuristic approaches have been successfully employed, including monarch butterfly optimization [62,63], slime mould algorithm [64], moth search algorithm [65,66], Harris hawks optimization [67][68][69][70], differential flower pollination [71], symbiotic organisms search [72], Henry gas solubility optimization [73], and satin bowerbird optimizer [74]. As can be seen from the literature, there is an increasing trend of hybridizing metaheuristics and machine learning to tackle complex problems in the field of engineering.…”

Section: Introductionmentioning

confidence: 99%

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Dinh

Nguyen

Tran

et al. 2021

Mathematical Problems in Engineering

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Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

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A new approach to predict the missing values of algae during water quality monitoring programs based on a hybrid moth search algorithm and the random vector functional link network

Cited by 27 publications

References 62 publications

Random vector functional link network: recent developments, applications, and future directions

Random vector functional link network: recent developments, applications, and future directions

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

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