GÜNLÜK ÇÖZÜNMÜŞ OKSİJEN KONSANTRASYONUNUN ÇOK DEĞİŞKENLİ UYARLANABİLİR REGRESYON EĞRİLERİ İLE TAHMİN EDİLMESİ

Yıl 2020, , 1479 - 1498, 31.12.2020

Sinan Nacar , Betül Mete , Adem Bayram

https://doi.org/10.17482/uumfd.750518

Cited By: 4

Öz

Bu çalışmada su sıcaklığı (T), özgül iletkenlik (Öİ) verilerinden hesaplanmış elektriksel iletkenlik (Eİ), pH ve debi (Q) verileri kullanılarak çok değişkenli uyarlanabilir regresyon eğrileri (MARS) ve regresyon analizi (RA) yöntemleri ile ÇO konsnatrasyonunun tahmin edilmesi amaçlanmıştır. MARS yönteminde en iyi tahmin değerlerini üreten temel fonksiyonlar ve denklemler belirlenmiş, RA yöntemi doğrusal, üs, üstel ve kuadratik olmak üzere dört farklı fonksiyona uygulanmış ve bu fonksiyonlara ait katsayılar hesaplanmıştır. Modelleme çalışmalarında Amerika Birleşik Devletleri’nin Oregon eyaletinin kuzey batısında yer alan Willamette Nehri’nin yan kollarından biri olan ve yaklaşık 2435 km2’lik bir havza alanına sahip Clackamas Nehri’ne ait Eylül 2016 − Ağustos 2017 dönemi günlük ortalama verileri kullanılmıştır. Her bir su kalitesi değişkeninin ÇO konsantrasyonu tahmin performansına etkisini belirlemek amacıyla sekiz farklı model oluşturulmuştur. ÇO konsantrasyonu tahmininde kurulan modellerin ve kullanılan yöntemlerin performanslarının değerlendirilebilmesi için çeşitli istatistikler (ortalama karesel hatanın karekökü, ortalama mutlak hata, saçılım indeksi ve Nash Sutcliffe verimlilik katsayısı) kullanılmıştır. Modelleme çalışmalarından elde edilen sonuçlar irdelendiğinde, MARS yönteminin RA yönteminden daha iyi sonuçlar verdiği anlaşılmıştır. Regresyon fonksiyonları içerisinden ise en başarılı tahmin sonuçlarının kuadratik fonksiyondan elde edildikleri ve MARS yöntemi ile elde edilen değerlere de oldukça yakın oldukları görülmüştür. ÇO konsantrasyonu tahmininde en etkili değişkenlerin T ve Q oldukları dolayısıyla en etkisiz değişkenlerin ise Eİ ve pH oldukları anlaşılmıştır. Model 3, Model 5, Model 7 ve Model 8’den elde edilen sonuçların birbirine çok yakın olması sebebiyle daha az değişken ile güçlü tahminler yapması ve daha sade bir model olması bakımından ÇO tahmininde Model 3’ün kullanılmasının daha avantajlı olacağı sonucuna varılmıştır. 

Anahtar Kelimeler

Çok değişkenli uyarlanabilir regresyon eğrileri, Çözünmüş oksijen konsantrasyonu, Regresyon analizi

Estimation of Daily Dissolved Oxygen Concentration using Multivariate Adaptive Regression Splines Methods

Öz

In this study, it is aimed to estimate DO concentration using the river water temperature (WT), electrical conductivity (EC) computed from specific conductance (SC), pH, and discharge (Q) data by employing multivariate adaptive regression splines (MARS) and regression analysis (RA) methods. For this purpose, the basic functions and equations, which yielded the best estimation values in the MARS method, were determined. The RA method was applied to four different functions, namely linear, power, exponential, and quadratic, and the coefficients for these functions were computed. Daily mean data for a period from September 2016 to August 2017 were used in DO modeling studies for the Clackamas River having a basin area of approximately 2435 km2, which is one of the tributaries of the Willamette River located in the northwestern state of Oregon, USA. Eight different models were generated to determine the effect of each water-quality parameter on the estimation performance of the river DO concentration. In order to evaluate the performances of the methods and the models used in estimating the river DO concentration, various statistics, e.g. the root mean square error, mean absolute error, scatter index, and Nash Sutcliffe coefficient of efficiency, were used. When the results from the modeling efforts were evaluated, it was seen that the MARS method provided better results than RA method. It was also seen that the most successful estimation results were provided by quadratic function among the regression functions and were also quite close to estimation results provided by the MARS method. It was revealed WT and Q parameters were highly effective, that is to say, EC and pH parameters were highly ineffective in estimating the river DO concentration. The estimation results obtained from Model 3, Model 5, Model7, and Model 8 were very close to each other. It was concluded that Model 3 with less parameters would be more advantageous to use in the estimation of the river DO concentration owing to being a simpler model but making strong estimations.

Anahtar Kelimeler

Multivariate adaptive regression splines, Dissolved oxygen concentration, Regression analysis

Kaynakça

• Akbal, F., Gurel, L., Bahadir, T., Guler, I., Bakan, G. ve Buyukgungor, H. (2011) Multivariate statistical techniques for the assessment of surface water quality at the mid-black sea coast of Turkey, Water, Air, and Soil Pollution, 216(1-4), 21-37.
• Antanasijevic, D., Pocajt, V., Povrenovic, D., Peric-Grujic, A. ve Ristic, M. (2013) Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environmental Science and Pollution Research, 20(12), 9006-9013.
• Ay, M. ve Kisi, O. (2012) Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado, Journal of Environmental Engineering, 138(6), 654-662.
• Ay, M. ve Kisi, O. (2017) Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques, KSCE Journal of Civil Engineering, 21(5), 1631-1639.
• Bayazıt, M., (1981) Hidrolojide istatistik yöntemler, İTÜ Matbaası, Gümüşsuyu, İstanbul.
• Bayazıt, M., Yeğen Oğuz, B. (2005) Mühendisler için istatistik, Birsen Yayınevi, İstanbul.
• Bayram, A. ve Kankal, M. (2015) Artificial neural network modeling of dissolved oxygen concentration in a Turkish Watershed, Polish Journal of Environmental Studies, 24(4), 1507-1515.
• Bayram, A., Kankal, M. ve Onsoy, H. (2012) Estimation of suspended sediment concentration from turbidity measurements using artificial neural networks, Environmental Monitoring and Assessment, 184(7), 4355-4365.
• Bu, H., Tan, X., Li, S. ve Zhang, Q. (2010) Water quality assessment of the Jinshui River (China) using multivariate statistical techniques, Environmental Earth Sciences, 60(8), 1631-1639.
• Carpenter, K.D. (2003) Water-quality and algal conditions in the Clackamas River Basin, Oregon ve their relations to land and water management, US Department of the Interior, US Geological Survey, 2, (4189).
• Chen, L.H. ve Li, L. (2008) Evaluation of dissolved oxygen in water by artificial neural network and sample optimization, Journal of Central South University of Technology, 15(2), 416-420.
• Cox, B.A. (2003a) A review of dissolved oxygen modelling techniques for lowland rivers, Science of the Total Environment, 314, 303-334.
• Cox, B.A. (2003b) A review of currently available in-stream water-quality models and their applicability for simulating dissolved oxygen in lowland rivers, Science of the Total Environment, 314, 335-377.
• Csabragi, A., Molnar, S., Tanos, P., Kovacs, J., Molnar, M., Szabo, I. ve Hatvani, I. G. (2019) Estimation of dissolved oxygen in riverine ecosystems: Comparison of differently optimized neural networks, Ecological Engineering, 138, 298-309.
• Elkiran, G., Nourani, V., Abba, S. I. ve Abdullahi, J. (2018) Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river, Global Journal of Environmental Science and Management, 4(4), 439-450.
• EPA. (1986). Quality Criteria for Water. Washington DC: Office of Water Regulations and Standards.
• Fetene, B.N., Shufen, R. ve Dixit, U.S. (2018) FEM-based neural network modeling of laser-assisted bending, Neural Computing and Applications, 29(6), 69-82.
• Friedman, J.H. (1991) Multivariate adaptive regression splines, The Annals of Statistics, 19(1), 1-67.
• Fondriest Environmental, Inc. “Dissolved Oxygen.” Fundamentals of Environmental Measurements. 19 Nov. 2013. Web. < https://www.fondriest.com/environmental-measurements/parameters/water-quality/dissolved-oxygen/ >.
• Heddam, S. (2014a) Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study, Environmental Monitoring and Assessment, 186(1), 597-619.
• Heddam, S. (2014b) Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA, Environmental Technology, 35(13), 1650-1657.
• Heddam, S. (2014c) Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study, Environmental Monitoring and Assessment, 186(1), 597-619.
• Heddam, S. (2016) Use of optimally pruned extreme learning machine (OP-ELM) in forecasting dissolved oxygen concentration (DO) several hours in advance: a case study from the Klamath River, Oregon, USA, Environmental Processes, 3(4), 909-937.
• Heddam, S. ve Kisi, O. (2018) Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree, Journal of Hydrology, 559, 499-509.
• Kalff, J. (2002) Limnology: Inland Water Ecosystems, Prentice-Hall, New Jersey, USA, 1-92.
• Kanda, E. K., Kosgei, J. R. ve Kipkorir, E. C. (2015) Simulation of organic carbon loading using MIKE 11 model: a case of River Nzoia, Kenya, Water Practice and Technology, 10(2), 298-304.
• Kanda, E., Kipkorir, E. ve Kosgei, J. (2016) Dissolved oxygen modelling using artificial neural network: a case of River Nzoia, Lake Victoria basin, Kenya, Journal of Water Security, 2, jws2016004, 1-7.
• Khani, S. ve Rajaee, T. (2017) Modeling of dissolved oxygen concentration and its hysteresis behavior in rivers using wavelet transform‐based hybrid models, CLEAN–Soil, Air, Water, 45(2), 1500395, 1-19.
• Kisi, O., Akbari, N., Sanatipour, M., Hashemi, A., Teimourzadeh, K. ve Shiri, J. (2013) Modeling of dissolved oxygen in river water using artificial ıntelligence techniques, Journal of Environmental Informatics, 22(2), 92-101.
• Kisi, O. (2015). Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology, 528, 312-320.
• Kisi, O., Parmar, K. S., Soni, K., ve Demir, V. (2017). Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models. Air Quality, Atmosphere & Health, 10(7), 873-883.
• Kisi, O., Alizamir, M. ve Gorgij, A. D. (2020) Dissolved oxygen prediction using a new ensemble method, Environmental Science and Pollution Research, 27(9), 9589-9603.
• Lewis M.E. (2006) Dissolved oxygen. Version 2.0, Chapter A6, section 6.2, techniques of water-resources investigations, Book 9, US Geological Survey.
• Liu, B., Wang, W.L., Han, R.M., Sheng, M., Ye, L.L., Du, X., Wu, X.T., Wang, G.X. ve Wang, G.X. (2016) Dynamics of dissolved oxygen and the affecting factors in sediment of polluted urban rivers under aeration treatment, Water, Air, and Soil Pollution, 227(6), 172, 1-13
• Mulholland, P.J., Houser, J.N. ve Maloney, K.O. (2005) Stream diurnal dissolved oxygen profiles as indicators of in-stream metabolism and disturbance effects: Fort Benning as a case study, Ecological Indicators, 5(3), 243-252.
• Murrell, M.C., Caffrey, J.M., Marcovich, D.T., Beck, M.W., Jarvis, B.M. ve Hagy, J.D. (2018) Seasonal oxygen dynamics in a warm temperate estuary: effects of hydrologic variability on measurements of primary production, respiration, and net metabolism, Estuaries and Coasts, 41(3), 690-707.
• Nacar, S., Bayram, A., Satılmış, U. ve Baki, O.T. (2016) The surface water quality monitoring and assessment of the Eastern Black Sea Basin (Trabzon Province) streams, Turkey, 12. International Congress on Advances in Civil Engineering (Full text in CD) İstanbul, Turkey 21-23 September Istanbul, 1-6.
• Nacar, S., Hinis, M.A. ve Kankal, M. (2018a) Forecasting daily streamflow discharges using various neural network models and training algorithms, KSCE Journal of Civil Engineering, 22(9), 3676-3685.
• Nacar, S., Kankal, M. ve Hınıs, M.A. (2018b) Çok değişkenli uyarlanabilir regresyon eğrileri (ÇDURE) ile günlük akarsu akımlarının tahmini-haldizen deresi örneği, Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 8(1), 38-47.
• Nacar, S., Bayram, A., Baki, O. T., Kankal, M., ve Aras, E. (2020). Spatial forecasting of dissolved oxygen concentration in the Eastern Black Sea Basin, Turkey. Water, 12(4), 1041, 1-23.
• Najah, A., El-Shafie, A., Karim, O.A. ve El-Shafie, A.H. (2014) Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring, Environmental Science and Pollution Research, 21(3), 1658-1670.
• Nemati, S., Fazelifard, M. H., Terzi, O. ve Ghorbani, M. A. (2015) Estimation of dissolved oxygen using data-driven techniques in the Tai Po River, Hong Kong, Environmental Earth Sciences, 74(5), 4065-4073.
• Olyaie, E., Abyaneh, H. Z. ve Mehr, A. D. (2017) A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River, Geoscience Frontiers, 8(3), 517-527.
• Özfalcı, Y., (2008) Çok Değişkenli Uyarlanabilir Regresyon Kesitleri: Mars, Yüksek Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
• Palani, S., Liong, S.Y. ve Tkalich, P. (2008) An ANN application for water quality forecasting, Marine Pollution Bulletin, 56(9), 1586-1597.
• Panepinto, D. ve Genon, G. (2010) Modeling of Po river water quality in Torino (Italy), Water Resources Management, 24(12), 2937-2958.
• Post, C.J., Cope, M.P., Gerard, P.D., Masto, N.M., Vine, J.R., Stiglitz, R.Y., Hallstrom, J.O., Newman, J.C. ve Mikhailova, E.A. (2018) Monitoring spatial and temporal variation of dissolved oxygen and water temperature in the Savannah River using a sensor network, Environmental Monitoring and Assessment, 190(5), 272, 1-14.
• Radwan, M., Willems, P., El‐Sadek, A. ve Berlamont, J. (2003) Modelling of dissolved oxygen and biochemical oxygen demand in river water using a detailed and a simplified model, International Journal of River Basin Management, 1(2), 97-103.
• Rankovic, V., Radulovic, J., Radojevic, I., Ostojic, A. ve Comic, L. (2010) Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia, Ecological Modelling, 221(8), 1239-1244.
• Samui, P. (2013). Multivariate adaptive regression spline (Mars) for prediction of elastic modulus of jointed rock mass. Geotechnical and Geological Engineering, 31(1), 249-253.
• Sanchez, E., Colmenarejo, M.F., Vicente, J., Rubio, A., Garcia, M.G., Travieso, L. ve Borja, R. (2007) Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution, Ecological Indicators, 7(2), 315-328.
• Sarkar, A. ve Pandey, P. (2015) River water quality modelling using artificial neural network technique, Aquatic Procedia, 4, 1070-1077.
• Singh, K.P., Basant, A., Malik, A. ve Jain, G. (2009) Artificial neural network modeling of the river water quality-a case study, Ecological Modelling, 220(6), 888-895.
• Singh, K.P., Malik, A., Mohan, D. ve Sinha, S. (2004) Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India) a case study, Water Research, 38(18), 3980-3992.
• Spanou, M. ve Chen, D. (2000) An object-oriented tool for the control of point-source pollution in river systems, Environmental Modelling and Software, 15(1), 35-54.
• Şentürk, K. (2008) Akım Gözlem İstasyonu Olmayan Havzalarda su potansiyelinin belirlenmesi, Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
• Toprak, S. (2011) Çok değişkenli uyarlamalı regresyon eğrileri ve konik programlama ile zaman serilerinin modellenmesi, Yüksek Lisans Tezi, Dicle Üniversitesi, Fen Bilimleri Enstitüsü, Diyarbakır.
• Ünal, B. (2009) Çok değişkenli uyarlamalı regresyon uzanımları, Yüksek Lisans Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
• Wen, X., Fang, J., Diao, M. ve Zhang, C. (2013) Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China, Environmental Monitoring and Assessment, 185(5), 4361-4371.
• Yaseen, Z. M., Ehteram, M., Sharafati, A., Shahid, S., Al-Ansari, N. ve El-Shafie, A. (2018) The integration of nature-inspired algorithms with least square support vector regression models: application to modeling river dissolved oxygen concentration, Water, 10(9), 1124, 1-21.
• Yilmaz, B., Aras, E., Nacar, S. ve Kankal, M. (2018) Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models, Science of the Total Environment, 639, 826-840.
• Zhang, Q., Li, Z., Zeng, G., Li, J., Fang, Y., Yuan, Q., Wang Y. ve Ye, F. (2009) Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China, Environmental Monitoring and Assessment, 152(1-4), 123-131.