Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model

被引:34
作者
Younes, Mohammad K. [1 ]
Nopiah, Z. M. [1 ]
Basri, N. E. Ahmad [1 ]
Basri, H. [1 ]
Abushammala, Mohammed F. M. [2 ]
Younes, Mohammed Y. [3 ]
机构
[1] Univ Kebangsaan Malaysia, Dept Civil & Struct Engn, Bangi 43600, Selangor, Malaysia
[2] Knowledge Oasis Muscat, Middle East Coll, Dept Civil Engn, PB 79, Al Rusayl 124, Oman
[3] King Faisal Univ, Dept Chem Engn, Ahsaa, Saudi Arabia
关键词
Solid waste forecasting; Adaptive neuro-fuzzy inference system; Landfill area estimation; Area conservation; NEURAL-NETWORK; GENERATION; PREDICTION; SYSTEMS; ENERGY; PERFORMANCE; MANAGEMENT; BENEFITS; CITY;
D O I
10.1016/j.wasman.2015.10.020
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Solid waste prediction is crucial for sustainable solid waste management. The collection of accurate waste data records is challenging in developing countries. Solid waste generation is usually correlated with economic, demographic and social factors. However, these factors are not constant due to population and economic growth. The objective of this research is to minimize the land requirements for solid waste disposal for implementation of the Malaysian vision of waste disposal options. This goal has been previously achieved by integrating the solid waste forecasting model, waste composition and the Malaysian vision. The modified adaptive neural fuzzy inference system (MANFIS) was employed to develop a solid waste prediction model and search for the optimum input factors. The performance of the model was evaluated using the root mean square error (RMSE) and the coefficient of determination (R-2). The model validation results are as follows: RMSE for training = 0.2678, RMSE for testing = 3.9860 and R-2 = 0.99. Implementation of the Malaysian vision for waste disposal options can minimize the land requirements for waste disposal by up to 43%. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:3 / 11
页数:9
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