Sensitivity analysis of artificial neural networks for just-suspension speed prediction in solid-liquid mixing systems: Performance comparison of MLPNN and RBFNN

Ibrahim, Shaliza; Choong, Choe Earn

doi:10.1016/j.aei.2019.02.004

Cited by 25 publications

(14 citation statements)

References 25 publications

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“…Finally, sensitivity analysis is recommended by researchers to check how sensitive is the model’s accuracy with each input parameter (Ibrahim et al 2019 ). Therefore, in this study, sensitivity analysis will be introduced to check the redundancy and to determine the significance of each input parameters on the accuracy of the proposed models based on Pearson’s correlation coefficient: r xy is the covariance correlation function, n is the sample size of the data.…”

Section: Methodsmentioning

confidence: 99%

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia

Najah

Teo

Fai

et al. 2021

Int. J. Environ. Sci. Technol.

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Global concerns have been observed due to the outbreak and lockdown causal-based COVID-19, and hence, a global pandemic was announced by the World Health Organization (WHO) in January 2020. The Movement Control Order (MCO) in Malaysia acts to moderate the spread of COVID-19 through the enacted measures. Furthermore, massive industrial, agricultural activities and human encroachment were significantly reduced following the MCO guidelines. In this study, first, a reconnaissance survey was carried out on the effects of MCO on the health conditions of two urban rivers (i.e., Rivers of Klang and Penang) in Malaysia. Secondly, the effect of MCO lockdown on the water quality index (WQI) of a lake (Putrajaya Lake) in Malaysia is considered in this study. Finally, four machine learning algorithms have been investigated to predict WQI and the class in Putrajaya Lake. The main observations based on the analysis showed that noticeable enhancements of varying degrees in the WQI had occurred in the two investigated rivers. With regard to Putrajaya Lake, there is a significant increase in the WQI Class I, from 24% in February 2020 to 94% during the MCO month of March 2020. For WQI prediction, Multi-layer Perceptron (MLP) outperformed other models in predicting the changes in the index with a high level of accuracy. For sensitivity analysis results, it is shown that NH3-N and COD play vital rule and contributing significantly to predicting the class of WQI, followed by BOD, while the remaining three parameters (i.e. pH, DO, and TSS) exhibit a low level of importance.

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Section: Methodsmentioning

confidence: 99%

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia

Najah

Teo

Fai

et al. 2021

Int. J. Environ. Sci. Technol.

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“…For the case studies herein, two AI models and one optimization algorithm are applied. The selected AI models are radial basis function neural network (RBFNN) and LSSVR, which have been used to solve many complex and nonlinear problems in engineering, because they have advantageous features that make them superior to other AI models 14–16 …”

Section: Introductionmentioning

confidence: 99%

Metaheuristics‐optimized ensemble system for predicting mechanical strength of reinforced concrete materials

Chou

Nguyen

2021

Struct Control Health Monit

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Summary This paper develops a novel artificial intelligence (AI)‐based approach, called the metaheuristics‐optimized ensemble system (MOES), to assist civil engineers significantly in achieving accurate estimations of the mechanical strength of reinforced concrete (RC) materials. MOES integrates the advantages of hybrid and ensemble models by combining a metaheuristic optimization algorithm and efficient AI models. The metaheuristic algorithm finds the optimal hyperparameters of individual AI techniques and simultaneously adjusts their weights to yield the best optimized‐weight‐ensemble model. Particularly, the developed MOES was established by integrating the forensic‐based investigation optimization algorithm, the radial basis function neural network, and the least squares support vector regression. Four case studies of predicting structural mechanics of RC beams were performed to evaluate the performance of MOES and compare it to those of other single AI models, conventional ensemble models, hybrid models, and empirical methods. The analytical results of cross‐validation reveal that MOES was the most reliable approach, achieving the best values of all performance evaluation indexes. The automated predictive analytics revealed the robustness, efficiency, and stability of MOES. Thus, the proposed approach is a highly promising tool for predicting the structural mechanics of RC beams. The success of MOES in estimating the mechanical strength of RC beams has redefined the way of optimizing an ensemble AI model, which is the primary contribution of this research to the relevant body of knowledge.

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“…Proses pembelajaran data deret waktu pada backpropagation dilakukan proses secara supervised dan berulang sehingga jaringan dapat mengenal output yang diharapakan [13]. Hasil prediksi pengujian menggunakan MSE untuk menilai selisih output jaringan dengan target yang diharapkan [14]. Kombinasi arsitektur jaringan dan parameter dilakukan untuk membandingkan nilai mse yang paling kecil sebagai nilai akurasi yang tinggi [15].…”

Section: Pendahuluanunclassified

Prediksi Tinggi Permukaan Air Waduk Menggunakan Artificial Neural Network Berbasis Sliding Window

Kartini

Abadi

Saragih

2021

RESTI

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The water level in the reservoir is an important factor in the operation of a hydroelectric turbine to control water overflow so that there is no excessive degradation. This water control has an influence on the performance and production of hydroelectric energy. The daily reservoir water level (tpaw) recording of PLTA Riam Kanan is carried out through a daily direct measurement and observation process on the reservoir measuring board which is recapitulated every month in excel form. This time series historical data continues to grow every day to become a data warehouse that is still useless if only stored. Extracting knowledge from the data warehouse can be done using one of the artificial neural network data mining techniques, namely backpropagation to predict the next day's tpaw. Historical data for the tpaw time series is presented with a sliding window concept approach based on the window sizes used, namely 7, 14, 21 and 28. The window size represents the number of days as an input layer variable in the backpropagation network architecture to predict the next day's tpaw. Some backpropagation network testing is carried out using a combination of the number of window sizes against the comparison of the amount of training data and test data on the network. The prediction results obtained with the smallest mean squared error (mse) in network testing is 0.000577 as a high accuracy value of the prediction results. The network architecture with the smallest mse using 28 input layers, 10 hidden layers and 1 output layer can be a knowledge that can help the hydropower plant as an alternative in making turbine operation decisions based on the predicted results of reservoir water level.

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Sensitivity analysis of artificial neural networks for just-suspension speed prediction in solid-liquid mixing systems: Performance comparison of MLPNN and RBFNN

Cited by 25 publications

References 25 publications

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia

Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia

Metaheuristics‐optimized ensemble system for predicting mechanical strength of reinforced concrete materials

Prediksi Tinggi Permukaan Air Waduk Menggunakan Artificial Neural Network Berbasis Sliding Window

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