Yapay Sinir Ağı Yaklaşımlarının Yeraltı Suyundaki Nitrat Konsantrasyonunu Tahmin Etme Performansı

Yıl 2021, Sayı: 27, 873 - 879, 30.11.2021

Asli Coban

https://doi.org/10.31590/ejosat.866497

Öz

Su temininde temel kaynaklardan olduğu için yeraltı suyundaki kirlilik konsantrasyonunun tahmini önemlidir. Nitrat (NO3-N) yeraltı suyu kirliliğinde iyi bilinen gösterge parametrelerinden birisidir. Yapay sinir ağları (YSA) geçmiş veriler kullanılarak yeraltı suyundaki nitrat konsantrasyonunu tahmin etmek için kullanılabilir. Bu çalışmada, literatürdeki bir kuyu analizinden türetilen örnek bir veri seti, altı farklı kuyu özelliğine (girdi parametrelerine) göre yeraltı suyunun nitrat konsantrasyonunu (hedef parametre) tahmin etmek için kullanılmıştır. Kuyuların farklı hidrojeolojik parametrelerinin nitrat konsantrasyonu üzerindeki etkisine ilk kez bu çalışmada dikkat çekilmiştir. BPNN ve GRNN olmak üzere iki farklı YSA yaklaşımının performansı, regresyon performansları üzerinden karşılaştırmalı olarak değerlendirilmektedir. YSA modellerinin regresyon sonuçlarına bakıldığında, bu veri seti ile GRNN (R = 0.99) algoritmasının BPNN (R = 0.98) algoritmasından biraz daha iyi çalıştığı sonucuna varılabilir. Korelasyon sonuçları, nitrat kirliliğini tahmin etmek için kuyuların en önemli özelliklerinin sırasıyla kuyu derinliği, su tablasının altındaki derinlik, elek üstü kil ve kuyu ızgarasına derinlik olduğunu göstermektedir. Ayrıca tüm bu özellikler kuyunun nitrat konsantrasyonu ile ters orantılıdır.

Anahtar Kelimeler

Yeraltı Suyu, Nitrat Kirliliği, Yapay Sinir Ağı, Regresyon, BPNN, GRNN

The Performance of Artificial Neural Network Approaches to Estimate the Nitrate Concentration in Groundwater

Öz

The estimation of the pollution concentration in groundwater is important, since it is one of the key resources of water supply. Nitrate (NO3-N) is one of the well-known indicator parameters in groundwater pollution. Using historical data, artificial neural networks can be utilized to estimate the nitrate concentration in groundwater. In this study, a sample dataset, which is derived from a survey analysis in the literature, is used to estimate the nitrate concentration of groundwater (i.e., target parameter) with respect to six different well characteristics (i.e., input parameters). The effect of different hydrogeological parameters of the wells on the nitrate concentration is focused for the first time in this study. The performance of two different ANN approaches, namely BPNN and GRNN, is evaluated comparatively by means of their regression performances. Considering regression results of ANN models, it can be concluded that the GRNN (R=0.99) algorithm works slightly better than the BPNN (R=0.98) algorithm with this dataset. Correlation results indicate that the most important characteristics of the wells to estimate the nitrate pollution are the well depth, depth below water table, clay above screen, and depth to well screen, respectively. Moreover, all these characteristics are inversely related to nitrate concentration of the well.

Anahtar Kelimeler

Groundwater, Nitrate Pollution, Artificial Neural Network, Regression, BPNN, GRNN

Kaynakça

• WWAP (World Water Assessment Programme), (2009). Water in a Changing World. World Water Development Report 3, Paris/London, UNESCO Publishing/Earthscan.
• Nas, B., & Berktay, A. (2006). Groundwater contamination by nitrates in the city of Konya, (Turkey): A GIS perspective. Journal of Environmental Management, 79, 30–37.
• Zhou, Z. (2015). A Global Assessment of Nitrate Contamination in Groundwater. Internship Report, Supervisor: N. Ansems and P. Torfs.
• WHO, (2011). Background Document for Development of Guidelines for Drinking Water Quality, Nitrate and nitrite in drinking-water. WHO/SDE/WSH/07.01/16/Rev/1.
• Motevalli, A., Naghibi, S.A., Hashemi, H., Berndtsson, R., Pradhan, B., & Gholami, V. (2019). Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater. Journal of Cleaner Production, 228, 1248-1263.
• Kaddour, K., El Hacen, B., Hlima, D., & Yasmina, D. (2018). Groundwater vulnerability assessment using GOD method in Boulimat coastal District of Bejaia area North east Algeria. Journal of Biodiversity and Environmental Sciences, 13(3), 109-116.
• Pociene, A., & Pocius, S. (2005). Relationship between nitrate amount in groundwater and natural factors. Journal of Environmental Engineering and Landscape Management, 13(1), 23-30.
• Brown Jr., E.G., Rodriquez, M., & Ingenito, M. B. (2014). Well Design and Construction for Monitoring Groundwater at Contaminated Sites. Department of Toxic Substances Control, California Environmental Protection Agency, Final.
• Khalil, A., Almasri, M.N., McKee, M., & Kaluarachchi, J. (2005). Applicability of statistical learning algorithms in groundwater quality modeling. Water Resources Research, 41 (W05010), 1-16.
• Arslan, M. & Terzioğlu, H. (2020). Estimation of Solar Radiation Value using Artificial Intelligence Networks. European Journal of Science and Technology, (Special Issue), 488-497.
• Suen, J.-P., & Eheart, J.W. (2003). Evaluation of neural networks for modeling nitrate concentrations in rivers. Journal of Water Resources Planning and Management, 129, 505–510.
• Yesilnacar, M.I., Sahinkaya, E., Naz, M., & Ozkaya, B. (2008). Neural network prediction of nitrate in groundwater of Harran Plain, Turkey. Environmental Geology, 56, 19–25.
• Benzer, R., & Benzer, S. (2018). Forecasting the Nitrate Pollution of Groundwater and Surface Waters: Kütahya Example. Karaelmas Science and Engineering Journal, 8(1), 279-287. (in Turkish)
• Wagh, V., Panaskar, D., Muley, A., Mukate, S., & Gaikwad, S. (2018). Neural network modelling for nitrate concentration in groundwater of Kadava River basin, Nashik, Maharashtra, India. Groundwater for Sustainable Development, 7, 436–445.
• Huang, J., Xu, J., Liu, X., Liu, J., & Wang, L., (2011). Spatial distribution pattern analysis of groundwater nitrate nitrogen pollution in Shandong intensive farming regions of China using neural network method. Mathematical and Computer Modelling, 54, 995-1004.
• Ehteshami, M., Farahani, N. D., & Tavassoli, S. (2016). Simulation of nitrate contamination in groundwater using artificial neural networks. Modeling Earth Systems and Environment, 2(28), 1-10.
• Nolan, B.T., Fienen, M.N., & Lorenz, D.L. (2015). A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA. Journal of Hydrology, 531, 902-911.
• Darwishe, H., El Khattabi, J., Chaaban, F., Louche, B., Masson, E., & Carlier, E. (2017). Prediction and control of nitrate concentrations in groundwater by implementing a model based on GIS and artificial neural networks (ANN). Environmental Earth Sciences, 76(649), 1-14.
• Ouedraogo, I., Defourny, P., & Vanclooster, M. (2019). Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale. Hydrogeology Journal, 27, 1081–1098.
• Band, S.S., Janizadeh, S., Pal, S.C., Chowdhuri, I., Siabi, Z., Norouzi, A., Melesse, A.M., Shokri, M., & Mosavi, A. (2020). Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration. Sensors, 20(5763), 1-23.
• Zaqoot, H. A., Hamada, M., & Miqdad, S. (2018). A Comparative Study Of Ann For Predicting Nitrate Concentration In Groundwater Wells In The Southern Area Of Gaza Strip. Applied Artificial Intelligence, 32(7-8), 727-744.
• Alagha, J.S., Said, M.A.M., & Mogheir, Y. (2014). Modeling of nitrate concentration in groundwater using artificial intelligence approach - a case study of Gaza coastal aquifer. Environmental Monitoring and Assessment, 186, 35-45.
• Al-Mahallawi, K., Mania, J., Hani, A., & Shahrour, I. (2012). Using of neural networks for the prediction of nitrate groundwater contamination in rural and agricultural areas. Environmental Earth Sciences, 65, 917-928.
• Arabgol, R., Sartaj, M., & Ashgari, K. (2016). Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) Model. Environmental Modeling and Assessment, 21, 71-82.
• Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J., & Wang, J. (2020). Tackling environmental challenges in pollution controls using artificial intelligence: A review. Science of the Total Environment, 699(134279), 1-28.
• Townsend, M. A., & Young, D.P. (1995). Factors Affecting Nitrate Concentrations in Ground Water in Stafford County, Kansas. 238, 1-9.
• Jain, Y.K., & Bhandre, S.K. (2011). Min Max Normalization Based Data Perturbation Method for Privacy Protection. International Journal of Communication and Computer Technologies, 2(8), 45-50.
• Wagh, V.M., Panaskar, D.B., & Muley, A.A. (2017). Estimation of nitrate concentration in groundwater of Kadava river basin-Nashik district, Maharashtra, India by using artificial neural network model. Modeling Earth Systems and Environment, 3(36), 1-10.