期刊首页 优先出版 当期阅读 过刊浏览 作者中心 关于期刊 English

《工程(英文)》 >> 2021年 第7卷 第12期 doi: 10.1016/j.eng.2020.10.023

一种基于多因素分析和多模型集成的海洋溶解氧浓度时间序列预测混合神经网络模型

Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China

收稿日期: 2020-01-04 修回日期: 2020-07-25 录用日期: 2020-10-18 发布日期: 2021-03-19

下一篇 上一篇

摘要

溶解氧是水产养殖的重要指标,准确预测溶解氧浓度可有效提高水产品质量。本文提出了一种新的溶解氧混合预测模型,该模型包括多因素分析、自适应分解和优化集成三个阶段。首先,考虑到影响溶解氧浓度的因素复杂繁多,采用灰色关联度法筛选出与溶解氧关系最密切的环境因素,多因素的考虑使得模型融合更加有效。其次,运用经验小波变换方法自适应地将溶解氧、水温、盐度和氧饱和度等序列分解为子序列。然后,利用5个基准模型对经验小波变换分解出的子序列进行预测,这五个子预测模型的集成权重通过粒子群优化和引力搜索算法计算得出。最后,通过加权分配得到溶解氧多因素集成模型。来自太平洋岛屿海洋观测系统希洛WQB04站收集的时间序列数据验证了该模型的性能。实验的评价指标包括Nash-Sutcliffe效率系数、Kling-Gupta效率系数、平均绝对百分比误差、误差标准差和决定系数。实例分析表明:①所提出的模型能够获得优异的溶解氧预测结果;②该模型优于文中其他对比模型;③预测模型可用于分析溶解氧变化趋势,便于管理者能够做出更好的决策。

补充材料

图片

图1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献

[ 1 ] Yang Z. Watershed ecology and its applications. Engineering 2018;4(5):582–3. 链接1

[ 2 ] Hoogakker BAA, Lu Z, Umling N, Jones L, Zhou X, Rickaby RE, et al. Glacial expansion of oxygen-depleted seawater in the eastern tropical Pacific. Nature 2018;562(7727):410–3. 链接1

[ 3 ] McClanahan TR, Ateweberhan M, Muhando CA, Maina J, Mohammed MS. Effects of climate and seawater temperature variation on coral bleaching and mortality. Ecol Monogr 2007;77(4):503–25. 链接1

[ 4 ] Gimpel A, Stelzenmüller V, Grote B, Buck BH, Floeter J, Núñez-Riboni I, et al. A GIS modelling framework to evaluate marine spatial planning scenarios: colocation of offshore wind farms and aquaculture in the German EEZ. Mar Policy 2015;55:102–15. 链接1

[ 5 ] Keller AA, Ciannelli L, Wakefield WW, Simon V, Barth JA, Pierce SD. Occurrence of demersal fishes in relation to near-bottom oxygen levels within the California Current large marine ecosystem. Fish Oceanogr 2015;24(2):162–76. 链接1

[ 6 ] Addanki SC, Venkataraman H. Greening the economy: a review of urban sustainability measures for developing new cities. Sustainable Cities Soc 2017;32:1–8. 链接1

[ 7 ] Schmidtko S, Stramma L, Visbeck M. Decline in global oceanic oxygen content during the past five decades. Nature 2017;542(7641):335–9. 链接1

[ 8 ] Culberson SD, Piedrahita RH. Aquaculture pond ecosystem model: temperature and dissolved oxygen prediction—mechanism and application. Ecol Modell 1996;89(1–3):231–58. 链接1

[ 9 ] Liu S, Xu L, Li D, Li Q, Jiang Yu, Tai H, et al. Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization. Comput Electron Agric 2013;95:82–91. 链接1

[10] Faruk DÖ. A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intell 2010;23(4):586–94. 链接1

[11] Li C, Li Z, Wu J, Zhu L, Yue J. A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features. Inf Process Agric 2018;5(1):11–20. 链接1

[12] Huan J, Cao W, Qin Y. Prediction of dissolved oxygen in aquaculture based on EEMD and LSSVM optimized by the Bayesian evidence framework. Comput Electron Agric 2018;150:257–65. 链接1

[13] Khan U, Valeo C, Comparing A. Bayesian and fuzzy number approach to uncertainty quantification in short-term dissolved oxygen prediction. J Environ Inform 2017;30(1):1–16. 链接1

[14] Khan UT, Valeo C, He J. Non-linear fuzzy-set based uncertainty propagation for improved DO prediction using multiple-linear regression. Stochastic Environ Res Risk Assess 2013;27(3):599–616. 链接1

[15] Kisi O, Parmar KS. Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. J Hydrol 2016;534:104–12. 链接1

[16] Ay M, Kisi O. Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques. J Hydrol 2014;511:279–89. 链接1

[17] Ay M, Kisi O. Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado. J Environ Eng 2012;138(6):654–62. 链接1

[18] Zhu S, Heddam S. Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN). Water Quality Res J 2020;55(1):106–18. 链接1

[19] Heddam S, Kisi O. Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol 2018;559:499–509. 链接1

[20] Ay M, Kisi Ö. Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques. KSCE J Civ Eng 2017;21(5):1631–9. 链接1

[21] Khan UT, Valeo C. Dissolved oxygen prediction using a possibility theory based fuzzy neural network. Hydrol Earth Syst Sci 2016;20(6):2267–93. 链接1

[22] Shi P, Li G, Yuan Y, Huang G, Kuang L. Prediction of dissolved oxygen content in aquaculture using Clustering-based Softplus Extreme Learning Machine. Comput Electron Agric 2019;157:329–38. 链接1

[23] Ren Q, Zhang L, Wei Y, Li D. A method for predicting dissolved oxygen in aquaculture water in an aquaponics system. Comput Electron Agric 2018;151:384–91. 链接1

[24] Wu J, Li Z, Zhu L, Li G, Niu B, Peng F. Optimized BP neural network for dissolved oxygen prediction. IFAC-PapersOnLine 2018;51(17):596–601. 链接1

[25] Liu S, Xu L, Jiang Yu, Li D, Chen Y, Li Z. A hybrid WA–CPSO-LSSVR model for dissolved oxygen content prediction in crab culture. Eng Appl Artif Intell 2014;29:114–24. 链接1

[26] Raheli B, Aalami MT, El-Shafie A, Ghorbani MA, Deo RC. Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River. Environ Earth Sci 2017;76(14):503. 链接1

[27] Yang H, Csukás B, Varga M, Kucska B, Szabó T, Li D. A quick condition adaptive soft sensor model with dual scale structure for dissolved oxygen simulation of recirculation aquaculture system. Comput Electron Agric 2019;162:807–24. 链接1

[28] Ma J, Ding Y, Cheng JCP, Jiang F, Xu Z. Soft detection of 5-day BOD with sparse matrix in city harbor water using deep learning techniques. Water Res 2020;170:115350. 链接1

[29] Ren Q, Wang X, Li W, Wei Y, An D. Research of dissolved oxygen prediction in recirculating aquaculture systems based on deep belief network. Aquacult Eng 2020;90:102085. 链接1

[30] Kisi O, Akbari N, Sanatipour M, Hashemi A, Teimourzadeh K, Shiri J. Modeling of dissolved oxygen in river water using artificial intelligence techniques. J Environ Inform 2013;22(2):92–101. 链接1

[31] Li W, Wu H, Zhu N, Jiang Y, Tan J, Guo Y. Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU). Inf Process Agric 2020;8 (1):185–93. 链接1

[32] Ta X, Wei Y. Research on a dissolved oxygen prediction method for recirculating aquaculture systems based on a convolution neural network. Comput Electron Agric 2018;145:302–10. 链接1

[33] Mandal S, Debnath M, Ray S, Ghosh PB, Roy M, Ray S. Dynamic modelling of dissolved oxygen in the creeks of Sagar island, Hooghly–Matla estuarine system, West Bengal, India. Appl Math Model 2012;36(12):5952–63. 链接1

[34] Liu H, Yang R, Duan Z. Wind speed forecasting using a new multi-factor fusion and multi-resolution ensemble model with real-time decomposition and adaptive error correction. Energy Convers Manage 2020;217:112995. 链接1

[35] Liu H, Yang R, Wang T, Zhang L. A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections. Renew Energy 2021;165:573–94. 链接1

[36] Zounemat-Kermani M, Seo Y, Kim S, Ghorbani MA, Samadianfard S, Naghshara S, et al. Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River, Florida. Appl Sci 2019;9(12):2534. 链接1

[37] Gilles J. Empirical wavelet transform. IEEE Trans Signal Process 2013;61 (16):3999–4010. 链接1

[38] Zhu JJ, Kang L, Anderson PR. Predicting influent biochemical oxygen demand: balancing energy demand and risk management. Water Res 2018;128:304–13. 链接1

[39] Kisi O, Alizamir M, Gorgij AD. Dissolved oxygen prediction using a new ensemble method. Environ Sci Pollut Res Int 2020;27(9):9589–603. 链接1

[40] Cao W, Huan J, Liu C, Qin Y, Wu F. A combined model of dissolved oxygen prediction in the pond based on multiple-factor analysis and multi-scale feature extraction. Aquacult Eng 2019;84:50–9. 链接1

[41] Chatfield C, Weigend AS. Time series prediction: forecasting the future and understanding the past: Neil A. Gershenfeld and Andreas S. Weigend, 1994, ‘The future of time series’, in: A.S. Weigend and N.A. Gershenfeld, eds., (Addison-Wesley, Reading, MA), 1-70. Int J Forecast 1994;10(1):161–3.

[42] Chatfield C. The future of the time-series forecasting. Int J Forecast 1988;4 (3):411–9. 链接1

[43] Hawkins S, He H, Williams G, Baxter R. Outlier detection using replicator neural networks. In: Proceedings of International Conference on Data Warehousing and Knowledge Discovery; Kinsdale, Ireland. Berlin: Springer; 2002. p. 170–80. 链接1

[44] Hu J, Wang J, Zhang X, Fu Z. Research status and development trends of information technologies in aquacultures. Nongye Jixie Xuebao 2015;46 (7):251–63. 链接1

[45] Ip WC, Hu BQ, Wong H, Xia J. Applications of grey relational method to river environment quality evaluation in China. J Hydrol 2009;379(3–4):284–90. 链接1

[46] Valentini G, Dietterich TG. Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. J Mach Learn Res 2004;5:725–75. 链接1

[47] Nash JE, Sutcliffe JV. River flow forecasting through conceptual models part I— a discussion of principles. J Hydrol 1970;10(3):282–90. 链接1

[48] Knoben WJM, Freer JE, Woods RA. Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores. Hydrol Earth Syst Sci 2019;23(10):4323–31. 链接1

相关研究