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Comparison of temporal and spatial patterns of water quality parameters in Anzali Wetland (southwest of the Caspian Sea) using Support vector machine model | ||
| Caspian Journal of Environmental Sciences | ||
| دوره 19، شماره 1، فروردین 2021، صفحه 95-104 اصل مقاله (1.19 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22124/cjes.2021.4500 | ||
| نویسندگان | ||
| Maryam Fallah* 1؛ Ahmad Reza Pirali Zefrehei2؛ Seyyed Aliakbar Hedayati2؛ Tahereh Bagheri3 | ||
| 1Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran | ||
| 2Department of Aquatics Production and Exploitation, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 3Agricultural Research Education and Extension Organization, Iranian Fisheries Science Research Institute, Offshore Research Center, Tehran, Iran | ||
| چکیده | ||
| Urgent is growing to have reliable information from the country's water resources. In recent years, data mining models such as artificial neural network (ANN), gene expression programming, Bayesian network, machine algorithms, such as a support vector machine (SVM), and Random Forest have found widespread use in the field of simulation and prediction of components in aquatic ecosystems. Variables vary greatly on water quality parameters (due to nonlinear and complex relationships). Therefore, conventional methods are not eligible to solve water resource quality management problems. The aim of this study was to investigate the possibility of simulating the spatial and temporal alterations in water quality parameters during the period 1985-2014 in Anzali Wetland using a SVM model. Based on principal components analysis (PCA), the parameters EC, TDS, pH and BOD5 were selected for analysis in this study. Spearman correlation was calculated to determine the inputs of the model and the correlation coefficient(CC) between the water quality parameters. According to the results of the correlation table analysis, 8 types of structures including different inputs were used to predict the parameters with machine vector. In the next stage, 70% of the data were used to train, while the rest were used for analyzing the models. Criteria for determination coefficient (R2) and root mean square error (RMSE) were used for evaluation and model performance. The results revealed that in verification stage among different used models, the pH had the highest accuracy (0.95), while the lowest RMSE (0.20). Trend of alterations for optimal model of each parameter on a time scale, indicated an adequate estimation at most points. In general, the results exhibited the appropriate accuracy and acceptable performance of the SVM model in simulating water parameters. | ||
| کلیدواژه‌ها | ||
| Machine algorithms؛ PCA؛ RMSE؛ simulation؛ wetland | ||
| مراجع | ||
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Azari, F 2009, Determine the area of the protection (shield) of Anzali wetland, The Iranian Department of Environment, Tehran.105 p. [In Persian].
Bazargan-Lari, MR, Kerachian, R, Sedghi, H, Fallahnia M, Abed-Elmdoust A, Nikoo, MR, 2010, Developing Probabilistic Operating Rules for Real-time Conjunctive Use of Surface and Groundwater Resources: Application of Support Vector Machines, Journal of Water and Wastewater, 21:54-69. [In Persian].
Hsu, CW, Chang, CC & Lin, CJ 2003, A practical guide to support vector classification, www.csie.ntu.edu.tw, 4 May 2009.
Huang, S, Chang, J, Huang, Q & Chen, Y 2014, Monthly stream flow prediction using modified EMD-based support vector machine. Journal of Hydrology, 511: 4. 764-775.
Japan International Cooperation Agency (JICA) 2005, Integrated management for Anzali wetland. Department of Environment of Iran. Tehran, 182 p. [In Persian].
Kakaei Lafadani, E, Moghaddam Nia, A, Ahmadi, A, Jajarmizadeh, M & Ghafari, M 2013, Stream flow simulation using SVM, ANFIS and NAM models (A case study). Caspian journal of applied sciences research 2: 4. 86-93.
Liu, W, Wang, GY, Fu, JY, & Zou, X 2013, Water Quality Prediction Based on Improved Wavelet Transformation and Support Vector Machine. Advanced Materials Research, 726: 3547-3553.
Misra, D, Oommen, T, Agarwal, A, Mishra, SK & Thompson, AM 2009, Application and analysis of support vector machine based simulation for runoff and sediment yield. Biosystems Engineering, 103: 3. 527-535.
Mohammadpour, M, Mehrabi, A & Katouzi, M 2012, Daily discharge forecasting using support vector machine. Journal of Information and Electronics Engineering 2: 769-772.
Nayak, PC, Sudheer, KP, Rangan, DM & Ramasastri, KS 2004, A neuron-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291: 52-66.
Noori R, Morid S, Riahi, H 2009, Effect of Input Variables Preprocessing in Artificial Neural Network on Monthly Flow Prediction by PCA and Wavelet Transformation, Journal of Water and Wastewater, 20:13-22.
Noori R. Khakpoor a. Dehghani M. Farohknia A. 2011.Monthly Stream Flow Prediction Using Support Vector Machine Based on Principal Component Analysis, Journal of Water and Wastewater, 22: 118-123.
Pao, Y.1989."Adaptive pattern recognition and neural networks,"
Rafii, Y, Malekmohammadi, B, Abkar, A, Yavari, A, Ramezani Mehrian, M, Zohrabi, H 2011, Environmental Change Detection of Wetlands and Protected Areas Using Multi Temporal Images of TM Sensor (Case Study: Neyriz Wetland, Iran), Journal of environmental studies, 37:65-76.
Saedi, S, Soleimani, K, Nikmehr, S, Kavian, A 2016, Evaluation of the performance of the support vector machine model in predicting salinity changes aquifer in the Caspian Sea coastal, second national congress in the field of development of agricultural science and natural resources, 11p.
Solgi, A, Pourhaghi, A., Zarei, H, Ansari, H 2017, Modeling and Forecast Biological Oxygen Demand (BOD) using Combination Support Vector Machine with Wavelet Transform, Journal of Water and Soil, 31: 86-100.
Samsudin, R, Saad, P & Shabri, A 2011, River flow time series using least squares support vector machines. Hydrology and Earth System Sciences, 15: 1835-1852.
Sedighi, F, Vafakhah, M & Javadi, MR 2016, Rainfall–Runoff modeling using Support Vector Machine in snow-affected watershed. Arabian Journal for Science and Engineering, 41: 10. 4065-4076.
Tabachnick, BG & Fidell, LS 2001, Using multivariate statistics, 3rd Ed., Allyn and Bacon, Boston, London.
Vapnik, VN 1998, Statistical learning theory. Wiley, New York: 250-320.
Vapnik, VN 1995, The nature of statistical learning theory. Springer, New York: 250-320.
Xin, S, Qing, X, Lei, Y & Ning, L 2010, "A comparative study of eutrophication Evaluation Models Based on SOM Neural Network and SVM", Journal of Chongqing University (Natural Science Edition), 33:119-123.
Xiang, SL, Liu, ZM, Ma, LP 2006, Study of multivariate linear regression analysis model for ground water quality prediction. Guizhou Science, 24:60-2.
Yoon, H, Jun, SC, Hyun, Y, Bae, GO & Lee, KK 2011, A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology 396: 128-138. | ||
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