AI-based ensemble modeling of landfill leakage employing a lysimeter, climatic data and transfer learning

• Published in: Journal of hydrology (Amsterdam) 2022-09, Vol.612, p.128243, Article 128243
• Main Authors: Baghanam, Aida H., Vakili, Amirreza Tabataba, Nourani, Vahid, Dąbrowska, Dominika, Soltysiak, Marek
• Format: Article
• Language: English
• Subjects: Artificial Neural Network (ANN); Emotional ANN (EANN); Landfill leachate; Lysimeter; Neuro-Fuzzy Inference System (ANFIS); Poland; Tychy-Urbanowice landfill complex
• ISSN: 0022-1694
• DOI: 10.1016/j.jhydrol.2022.128243

•Field measurement of EC adjoined by artificial intelligence-based model to conduct leachate induced pollution in landfill.•To monitor the EC, waste temperature, and moisture, two lysimeter tests were conducted.•ANN, ANFIS and Emotional ANN (EANN) models were developed to determine the parameters affecting the EC.•Transfer learning method were used to predict the missing EC in a lysimeter. Predicting leachate pollutants is of prime importance in detecting the amount of pollution in water resources adjacent to sources of leakage. In this study, Electrical Conductivity (EC) as a physicochemical water pollution parameter with the possibility of portable measurement was used as an indicator of leachate quality for the Tychy-Urbanowice operating and closed landfill complex. In order to simulate landfill conditions, two lysimeter experiments were conducted simultaneously. Using sensors mounted in the lysimeters, from the end of November 2018 to the end of December 2019, EC, waste temperature and waste moisture were measured for the open lysimeter and only waste moisture for the closed lysimeter. Additionally, meteorological data obtained from the nearest synoptic station and soil moisture and temperature acquired from the GLDAS satellite were employed as external data to analyze various conditions. Thereafter, Artificial Neural Network (ANN), Neuro-Fuzzy Inference System (ANFIS), and Emotional ANN (EANN) models were developed to determine the parameters affecting the EC value recorded for the open lysimeter and subsequently, predict the missing EC parameter of the closed lysimeter by employing the transfer learning method. Following that, in order to improve the precision of EC predictions, ensemble techniques were applied to the outputs of the models that were developed. The results showed that the moisture of the lysimeters made a significant contribution to the EC value prediction. It is worth mentioning that among ANN, ANFIS, and EANN, the EANN model yielded more precise results in EC estimation, with the average DC above 0.80 and 0.90 for individual and ensembled modeling in both the training and verification phases, respectively.