Abstract
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model’s accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model—which applies two hidden layers—achieved the best performance, then followed by ANN-MLP1 model—which applies one hidden layer and three inputs—while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.
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References
Abbasi M, Abduli M, Omidvar B (2013) Forecasting municipal solid waste generation by hybrid support vector machine and partial least square model. Int J Environ Res 7(1):27–38
Abdallah M, Warith M, Narbaitz R, Petriu E, Kennedy K (2011) Combining fuzzy logic and neural networks in modeling landfill gas production. World Acad Sci Eng Technol 78, 559–565
Abunama T, Othman F, Alslaibi T, Abualqumboz M (2017) Quantifying the generated and percolated leachate through a landfill’s lining system in Gaza Strip, Palestine. Pol J Environ Stud 26(6):2455–2461. https://doi.org/10.15244/pjoes/73803
Abunama T, Othman F, Younes MK (2018) Predicting sanitary landfill leachate generation in humid regions using ANFIS modeling. Environ Monit Assess 190(10):597. https://doi.org/10.1007/s10661-018-6966-y
Abushammala MFM, Basri N, Kadhum A, Basri H, El-Shafie A, Mastura S (2014) Evaluation of methane generation rate and potential from selected landfills in Malaysia. Int J Environ Sci Technol 11(2):377–384. https://doi.org/10.1007/s13762-013-0197-0
Agamuthu P, Long K bin (2007) Evaluation of landfill cover systems under tropical conditions. Manuscript Reference, (07)
Agamuthu P, Venu Mahendra M, Mohd Afzanizam M (2011) Material flow analysis of aluminum in a dynamic system: Jeram sanitary landfill. Malaysian J Sci 30(1):16–27
Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sust Energ Rev 33:102–109. https://doi.org/10.1016/j.rser.2014.01.069
Ansari M, Othman F, Abunama T, El-Shafie A (2018) Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia. Environ Sci Pollut Res 25:12139–12149. https://doi.org/10.1007/s11356-018-1438-z
Ayele G, Teshale E, Yu B, Rutherfurd I, Jeong J (2017) Streamflow and sediment yield prediction for watershed prioritization in the Upper Blue Nile River Basin, Ethiopia. Water 9(10):782. https://doi.org/10.3390/w9100782
Aziz HA, Adlan MN, Amilin K, Yusoff MS, Ramly NH, Umar M (2012) Quantification of leachate generation rate from a semi-aerobic landfill in Malaysia. Environ Eng Manag J 11(9):1581–1585
Bagheri M, Bazvand A, Ehteshami M (2017) Application of artificial intelligence for the management of landfill leachate penetration into groundwater, and assessment of its environmental impacts. J Clean Prod 149:784–796. https://doi.org/10.1016/j.jclepro.2017.02.157
Berger KU (2015) On the current state of the hydrologic evaluation of landfill performance (HELP) model. Waste Manag 38:201–209
Bunsan S, Chen W-Y, Chen H-W, Chuang YH, Grisdanurak N (2013) Modeling the dioxin emission of a municipal solid waste incinerator using neural networks. Chemosphere 92(3):258–264. https://doi.org/10.1016/j.chemosphere.2013.01.083
Chapman SJ (2015) MATLAB programming for engineers. Cengage Learning US
Chen WB, Liu WC (2014) Artificial neural network modeling of dissolved oxygen in reservoir. Environ Monit Assess 186(2):1203–1217. https://doi.org/10.1007/s10661-013-3450-6
Chen Y, Wang Y, Xie H (2015) Breakthrough time-based design of landfill composite liners. Geotext Geomembr 43(2):196–206. https://doi.org/10.1016/J.GEOTEXMEM.2015.01.005
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1023/A:1022627411411
Dai C, Li YP, Huang GH (2011) A two-stage support-vector-regression optimization model for municipal solid waste management - a case study of Beijing, China. J Environ Manag 92(12):3023–3037. https://doi.org/10.1016/j.jenvman.2011.06.038
El-Fadel M, Findikakis A, Leckie J (1997) Modeling leachate generation and transport in solid waste landfills. Environ Technol 18(7):669–686
El-Shafie A, Abdin AE, Noureldin A, Taha MR (2009) Enhancing inflow forecasting model at Aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements. Water Resour Manag 23(11):2289–2315. https://doi.org/10.1007/s11269-008-9382-1
Ghorbani MA, Zadeh HA, Isazadeh M, Terzi O (2016) A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environ Earth Sci 75(6):476. https://doi.org/10.1007/s12665-015-5096-x
Grugnaletti M, Pantini S, Verginelli I, Lombardi F (2016) An easy-to-use tool for the evaluation of leachate production at landfill sites. Waste Manag 55:204–219
He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386. https://doi.org/10.1016/j.jhydrol.2013.11.054
Karaca F, Özkaya B (2006) NN-LEAP: a neural network-based model for controlling leachate flow-rate in a municipal solid waste landfill site. Environ Model Softw 21(8):1190–1197
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
Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Softw 25(8):891–909. https://doi.org/10.1016/J.ENVSOFT.2010.02.003
Malakahmad A, Abualqumboz MS, Kutty SRM, Abunama TJ (2017) Assessment of carbon footprint emissions and environmental concerns of solid waste treatment and disposal techniques; case study of Malaysia. Waste Manag 70:282–292. https://doi.org/10.1016/J.WASMAN.2017.08.044
Mohd Adnan S, Yusoff S, Piaw C (2013) Soil chemistry and pollution study of a closed landfill site at Ampar Tenang, Selangor, Malaysia. Waste Manag Res 31(6):599–612. https://doi.org/10.1177/0734242X13482031
Mustafa YA, Jaid GM, Alwared AI, Ebrahim M (2014) The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP. Environ Sci Pollut Res 21(12):7530–7537. https://doi.org/10.1007/s11356-014-2635-z
Nilam T, Ibrahim T, Mahmood NZ, Othman F (2016) Estimation of Leachate Generation from MSW Landfills in Selangor. AJMBES, 19(1), 43–48
Noori R, Abdoli MA, Farokhnia A, Abbasi M (2009) Results uncertainty of solid waste generation forecasting by hybrid of wavelet transform-ANFIS and wavelet transform-neural network. Expert Syst Appl 63(6):461. https://doi.org/10.1016/j.eswa.2016.08.001
Pal S, Mukherjee S, Ghosh S (2014) Estimation of the phenolic waste attenuation capacity of some fine-grained soils with the help of ANN modeling. Environ Sci Pollut Res 21(5):3524–3533. https://doi.org/10.1007/s11356-013-2315-4
Pantini S, Verginelli I, Lombardi F (2014) A new screening model for leachate production assessment at landfill sites. Int J Environ Sci Technol 11(6):1503–1516. https://doi.org/10.1007/s13762-013-0344-7
Perugu M, Singam AJ, Kamasani CSR (2013) Multiple linear correlation analysis of daily reference evapotranspiration. Water Resour Manag 27(5):1489–1500. https://doi.org/10.1007/s11269-012-0250-7
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536. https://doi.org/10.1038/323533a0
Sabour MR, Amiri A (2017) Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate. Waste Manag 65:54–62. https://doi.org/10.1016/j.wasman.2017.03.048
Schroeder PR, Dozier TS, Zappi PA McEnroe BM, Sjostrom JW & Peyton R L (1994) The hydrologic evaluation of landfill performance (HELP) model: engineering documentation for version 3. EPA/600/9-94/xxx, U.S. Environmental Protection Agency Risk Reduction Engineering Laboratory, Cincinnati, OH
Tan ST, Hashim H, Lim JS, Ho WS, Lee CT, Yan J (2014) Energy and emissions benefits of renewable energy derived from municipal solid waste: analysis of a low carbon scenario in Malaysia. Appl Energy 136:797–804. https://doi.org/10.1016/j.apenergy.2014.06.003
Tiew K-G, Ahmad Basri NE, Watanabe K, Abushammala MFM, Bin Ibrahim MT (2015) Assessment of the sustainability level of community waste recycling program in Malaysia. J Mater Cycles Waste, 17(3), 598–605. https://doi.org/10.1007/s10163-014-0273-7
Vaverková M, Adamcová D (2015) Long-term temperature monitoring of a municipal solid waste landfill. Pol J Environ Stud 24(3):1373–1378. https://doi.org/10.15244/pjoes/29940
Vithanage M, Wijesekara H, Mayakaduwa SS (2017) Isolation, purification and analysis of dissolved organic carbon from Gohagoda uncontrolled open dumpsite leachate, Sri Lanka. Environ Technol 38(13–14):1610–1618. https://doi.org/10.1080/09593330.2016.1235229
Wei X, Kusiak A, Sadat HR (2012) Prediction of influent flow rate: data-mining approach. J Energy Eng 139(2):118–123
Xie H, Chen Y, Zhan L, Chen R, Tang X, Chen R, Ke H (2009) Investigation of migration of pollutant at the base of Suzhou Qizishan landfill without a liner system. J. Zhejiang Univ. Sci. A., 10(3), 439–449. https://doi.org/10.1631/jzus.A0820299
Xie H, Jiang Y, Zhang C, Feng S (2015) An analytical model for volatile organic compound transport through a composite liner consisting of a geomembrane, a GCL, and a soil liner. Environ Sci Pollut Res 22(4):2824–2836. https://doi.org/10.1007/s11356-014-3565-5
Xie H, Chen Y, Thomas HR, Sedighi M, Masum SA, Ran Q (2016) Contaminant transport in the sub-surface soil of an uncontrolled landfill site in China: site investigation and two-dimensional numerical analysis. Environ Sci Pollut Res 23(3):2566–2575. https://doi.org/10.1007/s11356-015-5504-5
Xie H, Zhang C, Feng S, Wang Q, Yan H (2018) Analytical model for degradable organic contaminant transport through a GMB/GCL/AL system. J Environ Eng 144(3):04018006. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001338
Yaseen ZM, El-shafie A, Jaafar O, Afan HA, Sayl KN (2015) Artificial intelligence based models for stream-flow forecasting: 2000–2015. J Hydrol 530:829–844. https://doi.org/10.1016/j.jhydrol.2015.10.038
Younes MK, Nopiah Z, Basri N, Basri H, Abushammala M, Maulud K (2015) Prediction of municipal solid waste generation using nonlinear autoregressive network. Environ Monit Assess 187(12):753. https://doi.org/10.1007/s10661-015-4977-5
Younes M, Nopiah Z, Basri N, Basri H, Abushammala M, Maulud K (2016) Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model. Waste Manag 55:3–11. https://doi.org/10.1016/j.wasman.2015.10.020
Yu P-S, Chen S-T, Chang I-F (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3–4):704–716. https://doi.org/10.1016/J.JHYDROL.2006.01.021
Zade JG, Noori R (2008) Prediction of municipal solid waste generation by use of artificial neural network: a case study of Mashhad. Int J Environ Res 2(1):13–22
Zhan TLT, Guan C, Xie HJ, Chen YM (2014) Vertical migration of leachate pollutants in clayey soils beneath an uncontrolled landfill at Huainan, China: a field and theoretical investigation. Sci Total Environ 470–471:290–298. https://doi.org/10.1016/J.SCITOTENV.2013.09.081
Zhang Q, Tian B, Zhang X, Ghulam A, Fang C, He R (2013a) Investigation on characteristics of leachate and concentrated leachate in three landfill leachate treatment plants. Waste Manag 33(11):2277–2286. https://doi.org/10.1016/J.WASMAN.2013.07.021
Zhang W, Zhang G, Chen Y (2013b) Analyses on a high leachate mound in a landfill of municipal solid waste in China. Environ Earth Sci 70(4):1747–1752
Acknowledgments
We would also like to thank the UM Water Research Center for the support rendered. We are most grateful and would like to thank the reviewers for their valuable suggestions, which have led to substantial improvements to the article.
Funding
This study was financially supported by the University of Malaya Research Grant (FL001-13SUS, RP017C-15SUS) and the Ministry of Higher Education Fundamental Research Grant (FP016-2014A).
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Abunama, T., Othman, F., Ansari, M. et al. Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill. Environ Sci Pollut Res 26, 3368–3381 (2019). https://doi.org/10.1007/s11356-018-3749-5
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DOI: https://doi.org/10.1007/s11356-018-3749-5