Application of soft computing to predict water quality in wetland

Springer Science and Business Media LLC - Tập 28 - Trang 185-200 - 2020
Quoc Bao Pham1,2, Reza Mohammadpour3, Nguyen Thi Thuy Linh4,5, Meriame Mohajane6,7, Ameneh Pourjasem3, Saad Sh Sammen8, Duong Tran Anh9, Van Thai Nam10
1Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
3Department of Civil Engineering, Islamic Azad University, Estahban, Iran
4Institute of Research and Development, Duy Tan University, Danang, Vietnam
5Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang, Vietnam
6Soil and Environment Microbiology Team, Department of Biology, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
7Water Sciences and Environment Engineering Team, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco
8Department of Civil Engineering, College of Engineering, Diyala University, Diyala Governorate, Iraq
9Department of International Cooperation and Research, Van Lang University (VLU), Ho Chi Minh City, Vietnam
10Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

Tóm tắt

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

Tài liệu tham khảo

Abba S. I., Hadi S J, Saad Sh. Sammen, Salih S. Q., Rabiu Abdulkadir, Quoc Pham, Yaseen Z. M. (2020). Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.124974 Abrahão R, Carvalho M, Da Silva Jr W, Machado T, Gadelha C, Hernandez M (2007) Use of index analysis to evaluate the water quality of a stream receiving industrial effluents. Water SA 33. https://doi.org/10.4314/wsa.v33i4.52940 Adnan RM, Malik A, Kumar A, Parmar KS, Kisi O (2019) Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arab J Geosci 12. https://doi.org/10.1007/s12517-019-4781-6 Alitaleshi F, Daghbandan A (2019) Using a multi-objective optimal design of GMDH type neural networks to evaluate the quality of treated water in a water treatment plant. Desalin Water Treat 139:123–132. https://doi.org/10.5004/dwt.2019.23092 Alobaidy AMJ, Abid HS, Maulood BK (2010) Application of water quality index for assessment of Dokan Lake ecosystem, Kurdistan region, Iraq. J Water Res Protect 2(9):792–798. https://doi.org/10.4236/jwarp.2010.29093 Bordalo AA, Teixeira R, Wiebe WJ (2006) A water quality index applied to an international Shared River basin: the case of the Douro River. Environ Manag 38:910–920. https://doi.org/10.1007/s00267-004-0037-6 Buragohain M, Mahanta C (2008) A novel approach for ANFIS modelling based on full factorial design. Applied Soft Comput J 8:609–625. https://doi.org/10.1016/j.asoc.2007.03.010 Cong, L. W., Bahadori, A., Zhang, J. & Ahmad, Z. 2019. Prediction of Water Quality Index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM). International Journal of River Basin Management, 1-15. https://doi.org/10.1080/15715124.2019.1628030 Cude CG (2001) Oregon water quality index a tool for evaluating water quality management effectiveness. J Am Water Resour Assoc 37(1):125–137. https://doi.org/10.1111/j.1752-1688.2001.tb05480.x Daghbandan A, Khalatbari S, Abbasi MM (2019) Applying GMDH-type neural network for modeling and prediction of turbidity and free residual aluminium in drinking water. Desalin Water Treat 140:118–131. https://doi.org/10.5004/dwt.2019.23357 Debels P, Figueroa R, Urrutia R, Barra R, Niell X (2005) Evaluation of water quality in the Chillan River (Central Chile) using physicochemical parameters and a modified water quality index. Environ Monit Assess 110(1–3):301–322. https://doi.org/10.1007/s10661-005-8064-1 Department of Environment (2005) Malaysia environmental quality report. Ministry of Natural Resources and Environment, Petal-ing Jaya Dinius SH (1972) Social accounting system for evaluating water resource. Water Resour Res 8(5):1159–1177. https://doi.org/10.1029/wr008i005p01159 Diop L, Samadianfard S, Bodian A, Yaseen ZM, Ghorbani MA, Salimi H (2020) Annual rainfall forecasting using hybrid artificial intelligence model: integration of multilayer perceptron with whale optimization algorithm. Water Resour Manag 34:733–746. https://doi.org/10.1007/s11269-019-02473-8 Duffy JJ, Franklin MA (1975) A learning identification algorithm and its application to an environmental system. IEEE Transac Syst Man Cybernet 2:226–240. https://doi.org/10.1109/tsmc.1975.5408476 Ebtehaj I, Bonakdari H, Zaji AH, Azimi H, Khoshbin F (2015) GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs. Eng Sci Technol Int J 18:746–757. https://doi.org/10.1016/j.jestch.2015.04.012 Emamgholizadeh S, Kashi H, Marofpoor I, Zalaghi E (2014) Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. Int J Environ Sci Technol 11(3):645–656. https://doi.org/10.1007/s13762-013-0378-x Farlow SJ (1984) Self-organizing methods in modeling: GMDH type algorithms. Marcel Dekker, New York, pp 1–24 Fatemeh B, Ehteram M, Sammen SS, Panahi F, Othman F, EL-Shafie A (2020) Estimation of total dissolved solids (TDS) using new hybrid machine learning models. J Hydrol 587:124989. https://doi.org/10.1016/j.jhydrol.2020.124989 Gallo G, Perfilieva I, Spagnuolo M, Spinello S (1999) Geographical data analysis via mountain function. Int J Intell Syst 14:359–373. https://doi.org/10.1002/(sici)1098-111x(199904)14:4<359::aid-int2>3.0.co;2-d Gazzaz NM, Yusoff MK, Aris AZ, Juahir H, Ramli MF (2012) Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar Pollut Bull 64(11):2409–2420. https://doi.org/10.1016/j.marpolbul.2012.08.005 Ghorbani MA, Deo RC, Kim S, Hasanpour Kashani M, Karimi V, Izadkhah M (2020) Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia. Soft Comput 24:12079–12090. https://doi.org/10.1007/s00500-019-04648-2 Ha H, Stenstrom MK (2003) Identification of land use with water quality data in stormwater using a neural network. Water Res 37:4222–4230. https://doi.org/10.1016/s0043-1354(03)00344-0 Hadi SJ, Abba SI, Sammen SS, Salih SQ, Al-ansari N, Yaseen ZM (2019) Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation, pp 1–16 Hanh PTM, Sthiannopkao S, Ba DT, Kim K (2011) Development of water quality indexes to identify pollutants in Vietnam’s surface water. J Environ Eng 137(4):273–283. https://doi.org/10.1061/(asce)ee.1943-7870.0000314 Horton RK (1965) An index number system for rating water quality. J Water Pollut Control Fed 37(3):300–306 Ivakhnenko AG (1968) The group method of data handling, a rival of the method of stochastic approximation. Soviet Auto Control 13(3):43–55 Ivakhnenko AG, Svetal'skiy BK (1975) Self-organization of world dynamic model according to Forrester’s data and control synthesis by selecting the vertices of the hypercube of feasible controls. Soviet Auto Control 8(1):25–40 Jahanara A-A, Khodashenas SR (2019) Prediction of ground water table using NF-GMDH based evolutionary algorithms. KSCE J Civ Eng 23:5235–5243. https://doi.org/10.1007/s12205-019-0804-9 Jang JSR (1993) ANFIS: adaptive network based fuzzy inference system. IEEE Transac Syst Man Cyberne 23(3):665–685. https://doi.org/10.1109/21.256541 Kaab A, Sharifi M, Mobli H, Nabavi-Pelesaraei A, Chau KW (2019) Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production. Sci Total Environ 664:1005–1019. https://doi.org/10.1016/j.scitotenv.2019.02.004 Kadlec RH, Wallace S, Knight RL (2008) Treatment wetlands. Lewis Publishers, USA. https://doi.org/10.1201/9781420012514.ch1 Kalantary F, Ardalan H, Nariman-Zadeh N (2009). An investigation on the Su-NSPT correlation using GMDHtype neural networks and genetic algorithms. Eng Geol 2009;104(1/2):144–155. https://doi.org/10.1016/j.enggeo.2008.09.006 Kaurish FW, Younos T (2007) Developing a standardized water quality index for evaluating surface water quality 1. JAWRA J Am Water Res Assoc 43(2):533–545. https://doi.org/10.1111/j.1752-1688.2007.00042.x Li H, Zhang G, Sun G (2012) Simulation and evaluation of the water purification function of Zhalong wetland based on a combined water quantity-quality model. SCIENCE CHINA Technol Sci 55:1973–1981. https://doi.org/10.1007/s11431-012-4887-5 Liou SM, Lo SL, Wang SH (2004) A generalized water quality index for Taiwan. Environ Monit Assess 96:32–35. https://doi.org/10.1023/b:emas.0000031715.83752.a1 Liu M, Lu J (2014) Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river? Environ Sci Pollut Res 21(18):11036–11053. https://doi.org/10.1007/s11356-014-3046-x Malik A, Kumar A (2020) Meteorological drought prediction using heuristic approaches based on effective drought index: a case study in Uttarakhand. Arab J Geosci 13. https://doi.org/10.1007/s12517-020-5239-6 Mehrara M, Moeini A, Ahrari M, Erfanifard A (2009) Investigating the efficiency in oil futures market based on GMDH approach. Expert Syst Appl 36(4):7479–7483. https://doi.org/10.1016/j.eswa.2008.09.055 Mohammadpour R (2017) Prediction of local scour around complex piers using GEP and M5-tree. Arab J Geosci 10:416. https://doi.org/10.1007/s12517-017-3203-x Mohammadpour R, Ab Ghani A, Shaharuddin S, Kiat C, Chang NAZ (2014) Nitrogen removal assessment by multivariable statistical technique in free surface wetland. In: 13th international conference on urban drainage. Malaysia, Sarawak Mohammadpour R, Shaharuddin S, Chang C, Zakaria N, Ghani A, Chan N (2015) Prediction of water quality index in constructed wetlands using support vector machine. Environ Sci Pollut Res 22:6208–6219. https://doi.org/10.1007/s11356-014-3806-7 Mohammadpour R, Shaharuddin S, Zakaria N, Ghani A, Vakili M, Chan N (2016) Prediction of water quality index in free surface constructed wetlands. Environ Earth Sci 75:1–12. https://doi.org/10.1007/s12665-015-4905-6 Mohammadpour R, Asaie Z, Shojaeian MR, Sadeghzadeh M (2018) A hybrid of ANN and CLA to predict rainfall. Arab J Geosci 11:533. https://doi.org/10.1007/s12517-018-3804-z Najafzadeh M (2015) Neurofuzzy-based GMDH-PSO to predict maximum scour depth at equilibrium at culvert outlets. J Pipeline Syst Eng Prac 7(1):06015001. https://doi.org/10.1061/(asce)ps.1949-1204.0000204 Najafzadeh M, Barani G-A, Azamathulla HM (2014) Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling. Neural Comput & Applic 24(3–4):629–635. https://doi.org/10.1007/s00521-012-1258-x Niroobakhsh M (2012) Prediction of water quality parameter in Jajrood River basin: application of multi layer perceptron (MLP) perceptron and radial basis function networks of artificial neural networks (ANNs). Afr J Agric Res 7(29):4131–4139. https://doi.org/10.5897/AJAR11.1645 Niu M, Gan K, Sun S, Li F (2017) Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2. 5 concentration forecasting. J Environ Manag 196:110–118. https://doi.org/10.1016/j.jenvman.2017.02.071 Nourani V, Khanghah TR, Sayyadi M et al (2013) Application of the artificial neural network to monitor the quality of treated water. Int J Manag Inf Technol 2(2):38–45. https://doi.org/10.24297/ijmit.v3i1.1388 Pattanaik ML, Choudhary R, Kumar B (2019) Prediction of frictional characteristics of bituminous mixes using group method of data handling and multigene symbolic genetic programming. Eng Comput:1–14. https://doi.org/10.1007/s00366-019-00802-4 Pesce SF, Wunderlin DA (2000) Use of water quality indices to verify the impact of Cordóba City (Argentina) on Suquía River. Water Res 34(11):2915–2926. https://doi.org/10.1016/s0043-1354(00)00036-1 Raheli B, Aalami MT, El-Shafie A, Ghorbani MA, Deo RC (2017) 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 76:503. https://doi.org/10.1007/s12665-017-6842-z Sahu M, Mahapatra SS, Sahu HB, Patel RK (2011) Prediction of water quality index using Neuro fuzzy inference system. Water Qual Expo Health 3(3–4):175–191. https://doi.org/10.1007/s12403-011-0054-7 Sánchez E, Colmenarejo MF, Vicente J, Rubio A, García MG, Travieso L, Borja R (2007) Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution. Ecol Indic 7(2):315–328. https://doi.org/10.1016/j.ecolind.2006.02.005 Sengur, A. & Turkoglu, I. 2008. A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases. Expert Systems with Applications, 35, 1011-1020. Series E-Technological Sciences 55 (7), 1973–1981. https://doi.org/10.1016/j.eswa.2007.08.003 Singh A, Malik A, Kumar A, Kisi O (2018) Rainfall-runoff modelling in hilly watershed using heuristic approaches with gamma test. Arab J Geosci 11(11):1–12. https://doi.org/10.1007/s12517-018-3614-3 Srinivasan D (2008) Energy demand prediction using GMDH networks. Neurocomputing. 72(1–3):625–629. https://doi.org/10.1016/j.neucom.2008.08.006 Tikhamarine Y, Malik A, Kumar A, Souag-Gamane D, Kisi O (2019) Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrol Sci J 64(15):1824–1842. https://doi.org/10.1080/02626667.2019.1678750 Tsai T-M, Yen P-H (2017) GMDH algorithms applied to turbidity forecasting. Appl Water Sci 7:1151–1160. https://doi.org/10.1007/s13201-016-0458-4 Übeyli ED (2008) Teaching application of MATLAB fuzzy logic toolbox to modeling coplanar waveguides. Comput Appl Eng Educ 16:223–232. https://doi.org/10.1002/cae.20166 Wei-Bo C, Wen-Cheng L (2015) Water quality modeling in reservoirs using multivariate linear regression and two neural network models. Adv Artif Neural Syst 2015:1–12. https://doi.org/10.1155/2015/521721 Witczak M, Korbicz J, Mrugalski M, Patton R (2006) A GMDH neural network-based approach to robust fault diagnosis: application to the DAMADICS benchmark problem. Control Eng Pract 14(6):671–683. https://doi.org/10.1016/j.conengprac.2005.04.007 Yaseen ZM, Naganna SR, Sa’adi Z, Samui P, Ghorbani MA, Salih SQ, Shahid S (2020) Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms. Water Resour Manag 34:1075–1091. https://doi.org/10.1007/s11269-020-02484-w Ying LC, Pan MC (2008) Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers Manag 49:205–211. https://doi.org/10.1016/j.enconman.2007.06.015 Zhu W, Wang J, Zhang W, Sun D (2012) Short-term effects of air pollution on lower respiratory diseases and forecasting by the group method of data handling. Atmos Environ 51:29–38. https://doi.org/10.1016/j.atmosenv.2012.01.051 Zou Q, Xiong Q, Li Q, Yi H, Yu Y, Wu C (2020) A water quality prediction method based on the multi-time scale bidirectional long short-term memory network. Environ Sci Pollut Res 27:16853–16864. https://doi.org/10.1007/s11356-020-08087-7