2. T. Abunama, F. Othman, M. Ansari, A. El-Shafie, Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill, Environ. Sci. Pollut. Res., 26(4), 3368-3381(2019).
3. N. P. Cheremisinoff, Groundwater Remediation and Treatment Technologies, 1st ed., Noyes publications, New Jersey, U.S.A, pp. 259-261(1998).
4. H. Xie, Y. Chen, L. Zhan, R. Chen, X. Tang, R. Chen, H. KE, 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(2009).
5. M. A. Kamaruddin, M. S. Yusoff, H. A. Aziz, Y. T. Hung, Sustainable treatment of landfill leachate, Appl. Water Sci., 5(2), 113-126(2015).
6. E. D. Yildiz, K. Unlü, R. K. Rowe, Modelling leachate quality and quantity in municipal solid waste landfills, Waste Manag. Res., 22(2), 78-92(2004).
7. H. A. Aziz, M. N. Adlan, K. Amilin, M. S. Yusoff, N. H. Ramly, M. Umar, Quantification of leachate generation rate from a semi-aerobic landfill in Malaysia, Environ. Eng. Manag. J., 11(9), 1581-1585(2012).
8. T. M. Alslaibi, I. Abustan, Y. K. Mogheir, S. Afifi, Quantification of leachate discharged to groundwater using the water balance method and the hydrologic evaluation of landfill performance (HELP) model, Waste Manag. Res., 31(1), 50-59(2013).
9. S. B. Broichsitter, H. H. Gerke, R. Horn, Assessment of leachate production from a municipal solid-waste landfill through water-balance modeling, Geosci., 8(10), 372(2018).
10. L. Alibardi, R. Cossu, Solid Waste Landfilling, 1st ed., Elsevier, U.S.A, pp. 229-242(2018).
11. M. Abbasi, M. A. Abduli, B. Omidvar, A. Banghvand, Forecasting municipal solid waste generation by hybrid support vector machine and partial least square model, Int. J. Environ. Res., 7(1), 27-38(2013).
12. F. Karaca, B. Özkaya, 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(2006).
13. K. Ishii, M. Sato, S. Ochiai, Prediction of leachate quantity and quality from a landfill site by the long short-term memory model, J. Environ. Manage., 310, 114733(2022).
14. J. F. Steffensen, Interpolation, 2nd ed., Dover publications, U.S.A, pp. 1-10(2006).
15. S. H. Lim, S. J. Kim, Y. J. Park, N. H. Kwon, A deep learning-based time series model with missing value handling techniques to predict various types of liquid cargo traffic, Expert Syst. Appl., 184, 115532(2021).
16. K. Dashdondov, K. Jo, M. H. Kim, Linear interpolation and machine learning methods for gas leakage prediction base on multi-source data integration, J. Korean Soc. Cosmetol., 13(3), 33-41(2022).
17. F. Chollet, J. J. Allaire, Deep Learning with R., . 1st ed. Manning publications, U.S.A, 84-109(2018).
20. H. Junninen, H. Niska, K. Tuppurainen, J. Ruuskanen, M. Kolehmainen, Methods for imputation of missing values in air quality data sets, Atmos. Environ., 38(18), 2895-2907(2004).
21. N. M. Noor, M. M. A. B. Abdullah, A. S. Yahaya, N. A. Ramli, Comparison of linear interpolation method and mean method to replace the missing values in environmental data Set, Mater. Sci. Forum., 803, 278-281(2007).
22. S. C. Canale, R. P. Chapra, Numerical Methods for Engineers., . 7th ed. McGraw Hill Education, U.S.A, 444-491(1998).
23. L. Breiman, Random forests, Mach. Learn., 45, 5-32(2001).
24. W. S. McCulloch, W. H. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys., 5, 115-133(1943).
25. F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain, Psychol. Rev., 65(6), 386-408(1958).
26. K. H. Cho, S. Sthiannopkao, Y. A. Pachepsky, K. W. Kim, J. H. Kim, Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network, Water Res., 45(17), 5535-5544(2011).
27. S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput., 9(8), 1735-1780(1997).
28. F. A. Gers, J. Schmidhuber, F. Cummins, Learning to forget: Continual prediction with LSTM, Neural Comput., 12(10), 2451-2471(2000).
29. K. M. Kim, J. H. Ahn, Comparison of machine learning-based models for forecasting chlorophyll-a at Han River with feature importance analysis of input variable, J. Environ. Manage., 318, 115636(2021).
30. S. M. Lee, Y. G. Sun, J. Lee, D. Lee, E. I. Cho, D. H. Park, Y. B. Kim, I. Sim, J. Y. Kim, J. Inst. Internet Broadcast. Commun., 19(5), 79-85(2019).
31. K. H. Cho, B. V. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, Learning phrase representations using RNN encoder–decoder for statistical machine translation, arXiv preprint arXiv., 1724-1734(2014).
32. T. Abunama, F. Othman, M. K. Younes, Predicting sanitary landfill leachate generation in humid regions using ANFIS modeling, Environ. Monit. Assess., 190(10), 1-15(2018).