ارزیابی عملکرد مدل های شبکه عصبی مصنوعی و رگرسیون چندگانه در سنجش کربن آلی محلول در آب
محورهای موضوعی :
ارزیابی پی آمدهای محیط زیستی
طاهر احمدزاده
1
,
ناصر مهردادی
2
,
مجتبی اردستانی
3
,
اکبر باغوند
4
1 - دانش آموخته دکتری مهندسی محیط زیست، دانشکده تحصیلات تکمیلی محیط زیست، دانشگاه تهران، تهران، ایران *(مسوول مکاتبات).
2 - استاد و عضو هییت علمی مهندسی محیط زیست، دانشکده تحصیلات تکمیلی محیط زیست، دانشگاه تهران، تهران، ایران.
3 - استاد و عضو هییت علمی مهندسی محیط زیست، دانشکده تحصیلات تکمیلی محیط زیست، دانشگاه تهران، تهران، ایران.
4 - استاد و عضو هییت علمی مهندسی محیط زیست، دانشکده تحصیلات تکمیلی محیط زیست، دانشگاه تهران، تهران، ایران.
تاریخ دریافت : 1394/07/04
تاریخ پذیرش : 1394/12/16
تاریخ انتشار : 1398/01/01
کلید واژه:
مدل سازی,
کربن آلی محلول,
کیفیت منابع آب,
شبکه عصبی,
رگرسیون چندگانه,
چکیده مقاله :
چکیده زمینه و هدف: اندازه گیری و پایش کربن آلی در محیط های آبی یکی از شاخص های مهم کیفی در پروژه های مدیریت محیط زیست، پایش کیفی منابع آب و تامین آب شرب است. در این تحقیق، عملکرد مدل شبکه عصبی مصنوعی و مدل رگرسیون غیر خطی چندگانه باهدف سنجش پارامتر کربن آلی در منابع آب با حداکثر ضریب همبستگی محتمل و حداقل تعداد پارامترهای ورودی، مورد مطالعه و بهینهسازی قرار گرفت.روش بررسی: به این منظور مدل اولیه شبکه عصبی مصنوعی و رگرسیون غیر خطی چندگانه با کلیه پارامترهای ورودی برای دستیابی بهحداقل پارامترهای مورد نیاز تحت بهینه سازی به روش حذف ترتیبی قرار گرفت.یافته ها: آزمون صحت سنجی مدل بیانگر توافق خوبی میان سنجش کربن آلی محلول و مشاهدات واقعی بوده است. تحلیلنتایج نشان دهنده ی عملکرد قابل قبول مدل شبکه عصبی با درصد خطای متوسط 7 % و ضریب همبستگی 91/0 می باشد.بحث و نتیجه گیری: رفتار سنجی نتایج مدل سازی آشکار نمود که هرچند مدل رگرسیون چندگانه با درصد خطای متوسط 8 % و ضریب همبستگی 89/0 عملکرد نسبتاً ضعیف تری داشته است، اما سرعت اجرای بالا و عملکرد بهتر در شرایط بحرانی نشان از قابلیت بالای این مدل در سنجش کربن آلی در منابع آب با دامنه تغییرات کیفی زیاد دارد.
چکیده انگلیسی:
Abstract Background and Objective: Monitoring of organic carbon in water resources is a critical quality index in environmental management, water quality monitoring and drinking water projects. In this study, the performance and applicability of artificial neural network and multiple nonlinear regression modeling were investigated and optimized for the prediction of dissolved organic carbon. Method: Optimization was performed using backward elimination method with the highest probable correlation coefficient and minimum number of input parameters. Findings: Model verification showed a good agreement between the predicted organic carbon and actual observations. Results showed the acceptable performance of neural network model with the mean absolute error percentage of 7.6% and correlation coefficient of 0.91. Discussion and Conclusion: Further investigations unveiled that although the multiple regression model, with mean absolute error percentage of 8.4% and correlation coefficient of 0.89, seems to be less appealing but its fast run-time and better performance in critical conditions makes it a better choice for the prediction of organic carbon in aqueous solotions with high range of qualitative changes.
منابع و مأخذ:
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Volk C, Kaplan L A, Robinson J, Johnson B, Wood L, Zhu H W, Le Chevallier M, 2005, Fluctuations of dissolved organic matter in river used for drinking water and impacts on conventional treatment plant performance. Environmental Science and Technology 39(11):4258–4264.
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Singh K P, Malik A, Mohan D, Sinha S, 2004, Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)-A case study. Journal of Water Research 38(18):3980–3992.
Beckett R, Ranville J, 2006, Natural organic matter. In: Newcombe G Dixon D (eds) Interface Science in Drinking Water Treatment. Elsevier Ltd, pp 299–316.
Hou Y, Chu W, Ma M, 2012, Carbonaceous and nitrogenous disinfection by products formation in the surface and ground water treatment plants using Yellow River as water source. Journal of Environmental Sciences 24(7):1204–1209.
Bot A, Benites J, 2005, The importance of soil organic matter Key to drought-resistant soil and sustained food production, FAO Bulletin, 80, Rome, Italy.
Visco G, Campanella L, Nobili V, 2005, Organic carbons and DOC in waters: An overview of the international norm for its measurements. Microchemical Journal 79:185–191.
Matilainen A, Gjessing E T, Lahtinen T, Hed L, Bhatnagar A, Sillanpää M, 2011, An overview of the methods used in the characterisation of natural organic matter (NOM) in relation to drinking water treatment. Chemosphere 83(11):1431–1442.
Hargesheimer E, 2002, Online monitoring for drinking water utilities, American Water Works Assossiation, pp. 427.
Khataee A R, Zarei M, Pourhassan M, 2010, Bioremediation of Malachite Green from Contaminated Water by Three Microalgae: Neural networks Modelling. CLEAN – Soil Air Water 38(1):96–103.
Bucak I O, Karlik B, 2011, Detection of Drinking Water Quality Using CMAC Based Artificial Neural networks. Ekoloji 20(78):75–81.
Kulkarni P, Chellam S, 2010, Disinfection by–product formation following chlorination of drinking water: Artificial neural networks models and changes in speciation with treatment. Science of the Total Environment 408(19):4202–4210.
Najah A, El–Shafie A, Karim O A, Jaafar O, El–Shafie A H, 2011, An application of different artificial intelligences techniques for water quality prediction. International Journal of the Physical Sciences 22 (6):5298–5308.
Kunwar P S, Gupta S, 2012, Artificial intelligence based modelling for predicting the disinfection by–products in water. Chemometrics and Intelligent Laboratory Systems 114:122–131.
Yetilmezsoy K, Ozkaya B, Cakmakci M, 2011, Artificial intelligence–based prediction models for environmental engineering. Neural networks World 11(3):193–218.
Volk C, Kaplan L A, Robinson J, Johnson B, Wood L, Zhu H W, Le Chevallier M, 2005, Fluctuations of dissolved organic matter in river used for drinking water and impacts on conventional treatment plant performance. Environmental Science and Technology 39(11):4258–4264.
Ortiz–Rodríguez J M, Martínez–Blanco M R, Viramontes JMC, Vega–Carrillo H R, 2013, Robust Design of Artificial Neural Networks Methodology in Neutron Spectrometry. In: Suzuki K (ed) Artificial Neural networks–Architectures and Applications. InTech, Rijeka, pp 83–111.
Dreyfus G, Martinez J M, Samuelides M, Gordon M B, Badran F, Thiria S, Hérault L, 2002, Reseaux de Neurones – Méthodologie et applications. Eyrolles, Paris.
Govindaraju R S, 2000, Artificial neural networks in hydrology. Journal of Hydrologic Engineering 5(2):124–137.
Schweitzer R C, Morris J B, 2000, A Tutorial on Neural Networks Using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) Training Algorithm and Molecular Descriptors with Application to the Prediction of Dielectric Constants through the Development of Quantitative Structure Property Relationships (QSPRs). United States Army Research Laboratory ARL–TR–2155.
Palani S, Liong S, Tkalich P, 2008, An ANN application for water quality forecasting. Marine Pollution Bulletin 56(9):1586–1597.
Gemperline P, 2006, Practical guide to chemometrics 2nd edn. CRC, Boca Raton
Ahmadzadeh Kokya T, Farhadi Kh, AliMohammad Kalhori A, 2012, Optimized Dispersive Liquid–Liquid Microextraction and Determination of Sorbic Acid and Benzoic Acid in Beverage Samples by Gas Chromatography, Food Anal. Methods. 5:351–358.
Nissen S., et al., 2015, FANN, Fast Artificial Neural Network Library, available online at <http://leenissen.dk>, last visited on 23 July 2015.
Stephan DD, Werner J, Yeater RP, 2001, Essential regression and experimental design for chemists and engineers, MS Excel Add-in Software Package.
Lin T Y, Tseng C H, 2000, Optimum design for artificial neural networks: an example in a bicycle derailleur system. Journal of engineering applications of artificial intelligence 13:3–14.
Soong T T, 2004, Fundamentals of probability and statistics for engineers. John Wiley, Sons Inc, New York.
