An estimation of Jeram sanitary landfill lifespan using artificial neural network (ANN) modelling analysis / Nur Shafieza Azizan, Muhammad Ridzuan Tea Muhamad Ali Tea and Syahrul Fithry Senin

• Main Authors: Azizan, Nur Shafieza (Author), Muhamad Ali Tea, Muhammad Ridzuan Tea (Author), Senin, Syahrul Fithry (Author)
• Format: Book
• Published: Universiti Teknologi MARA Cawangan Pulau Pinang, 2022-03.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata
• Notes: https://ir.uitm.edu.my/id/eprint/62593/1/62593.pdf
• Keywords: Industrial research. Research and development; Municipal refuse. Solid wastes; Sanitary landfills

The ability to forecast the quantity of municipal solid waste (MSW) is critical for long-term coordination of MSW. Forecasting the amount of MSW is often difficult due to the lack of data, and even when data is available, it is frequently inaccurate. Therefore, planning and implementing sustainable solid waste management strategies is important to determine the accuracy of solid waste generation's prediction. With regards to the situation, waste prediction models have been conducted to verify the effectiveness of the models towards the prediction of solid waste generation. As one of the most effective non-linear models, the Artificial neural network (ANN) model has been effectively utilized in the prediction of municipal solid waste at the Jeram Sanitary Landfill in Selangor's state. Datasets of solid waste generation, population, number of trash truck trips, and oil price index were used as input to the model for 114 weeks between 2018 and 2020. The generated models' efficiency was measured using the mean square error (MSE) and coefficient of regression value (R-square). Both measurements showed a good accuracy with the lowest value of MSE at 6379.6, and high value of R-square at 0.91585. Based on the data from 2018 to 2020, The Jeram Sanitary Landfill is expected to last 9.6 years, according to the ANN model. The current study contributes in forecasting and allocating crucial resources that will be necessary in the future for effective solid waste management, as well as exploring alternate approaches to achieving longterm objectives.