CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Mumbai Zonal Centre, 89 B, Dr. A. B. Road, Worli, Mumbai 400 018, India.
CSIR- National Environmental Engineering Research Institute (CSIR-NEERI), Technology Development Centre, Nehru Marg, Nagpur 400 020, India.
Waste Manag. 2021 Feb 15;121:206-214. doi: 10.1016/j.wasman.2020.12.011. Epub 2020 Dec 25.
Municipal solid waste (MSW) generation is a multi-variable dependent process and hence its quantification is relatively not easy. The estimations for monthly MSW generation are required to provide theoretical guidelines for understanding and designing the disposal system. These estimations help in budgetary planning for the handling of future waste with optimized waste management system. This study forecasts the monthly MSW generation in Nagpur (India) for the year 2023 using non-linear autoregressive (NAR) models. The classical multiplicative decomposition model with simple linear regression in time series was constructed with maximum absolute error of 6.34% to overcome the problem of data availability. It was observed that NAR neural models were able to predict short-term monthly MSW generation with absolute maximum error of 6.45% (Model A) and 3.05% (Model B) for the observation period. It was also concluded that the variation in MSW generation was best captured when yearly lagged values were used for the construction of NAR model and coefficient of efficiency (E) was 0.99 and 0.97 during testing and validation, respectively. It was found that in the year 2023, the city will record minimum waste generation in the month of February and maximum in the month of December. For the year 2023, it had been estimated that the maximum 48504 ± 1569 tons of waste in December and minimum 39682 ± 471 tons in February will be generated. It had also been estimated that the minimum waste generation from the year 2017 to 2023 will increase by approximately 5345 tons.
城市固体废物（MSW）的产生是一个多变量的依赖过程，因此其量化相对不容易。每月 MSW 产生量的估计值是提供理解和设计处理系统的理论指导所必需的。这些估计值有助于为未来的废物处理进行预算规划，并优化废物管理系统。本研究使用非线性自回归（NAR）模型预测了印度那格浦尔市 2023 年的每月 MSW 产生量。构建了具有时间序列中简单线性回归的经典乘法分解模型，最大绝对误差为 6.34%，以克服数据可用性问题。结果表明，NAR 神经网络模型能够预测短期月度 MSW 产生量，其绝对最大误差分别为 6.45%（模型 A）和 3.05%（模型 B）。还得出结论，当使用 NAR 模型构建时，使用每年的滞后值可以最好地捕捉 MSW 产生量的变化，效率系数（E）在测试和验证期间分别为 0.99 和 0.97。结果发现，在 2023 年，该市将在 2 月记录到最低的废物产生量，在 12 月记录到最高的废物产生量。对于 2023 年，预计 12 月将产生最多 48504 ± 1569 吨废物，2 月将产生最少 39682 ± 471 吨废物。还估计，2017 年至 2023 年期间，废物产生量将增加约 5345 吨。
