【來自SCI的前沿論文】———Predictionof Zeta Potential of Decomposed Peat via Machine Learning:Comparative Study of Support Vector Machine and Artificial NeuralNetworks
作（著）者：陳福迪
導師：王斌
學術期刊或出版社名稱：InternationalJournal of Electrochemical Science
期刊簡介：InternationalJournal of ElectrochemicalScience期刊隸屬于ESG出版社，學科分類為電化學方向，影響因子IF=1.469.
DOI：WOS:000359200400002
論文主要内容：
Zeta potential iscrucial for practical applications in electrochemistry. However, theprecise deterimination of zeta potential of decomposed peat iscomplex and has high requirements to related instructments. Previousstudy shows that zeta potential of decomposed peat can be predictedby backpropagation (BP) neural network. However, it lacks availablecomparisons and neglects the importance of the decomposed stages ofpeat and the required training times. Here, to extend this research,we propose a series of novel machine learning techniques includingsupport vector machine (SVM) and artificial neural networks (ANNs) topredict the zeta potential of decomposed peat.
Figure 1. Main structure of a support vector machine.
Figure 2. Mainstructure of a support vector machine.
Four indicatorsincluding hydrated radius (nm), cation valence, concentration (mol/L)and pH are set as independent variables while zeta potential (mV) isset as the dependent variable. The SVM, general regression neuralnetwork (GRNN) and multilayer feed-forward neural networks (MLFNs)are developed in different decomposed stages, including the slightlydecomposed peat, the highly decomposed peat and all decomposed peat.
Results show thatseparating the models based on the decomposed stages have betterprediction results than taking all decomposed peat in one model.During our studies, the SVM is the best model for the prediction tothe slightly decomposed peat (RMS error: 2.37, training time: 1s),while the GRNN is the best model for the prediction to the highlydecomposed peat (RMS error: 2.20, training time: 1s).
Figure 3. Testingresults of three zeta potential prediction models. a) SVM for theslightly decomposed peat; b) GRNN for the highly decomposed peat; c)SVM for all decomposed peat.
作者簡介：陳福迪，伟德 官网海洋生物學2014級研究生，主要研究方向為生态毒理學建模，碩士期間曾獲得2015和2016年研究生國家獎學金，參與項目《基于IPv6的水産品物聯網試驗系統的設計與應用》，畢業論文題目為《有機磷酸酯對雙齒圍沙蠶幼體急性毒性預測模型的研究》。
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