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首页> 外文期刊>International journal of systems assurance engineering and management >Prediction of municipal solid waste generation for optimum planning and management with artificial neural network-case study: Faridabad City in Haryana State (India)
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Prediction of municipal solid waste generation for optimum planning and management with artificial neural network-case study: Faridabad City in Haryana State (India)

机译:通过人工神经网络对城市固体废物的产生进行最佳规划和管理预测-案例研究:哈里亚纳邦(印度)的法里达巴德市

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摘要

Abstract Accurate prediction of municipal solid waste generation has an important role in future planning and waste management system. The characteristics of the generated solid waste are different at different places (municipality to municipality or country to country). The accurate prediction of municipal solid waste (MSW) generation becomes a crucial task in modern era. Its prediction requires accurate MSW data. The aim of the present study is to design the time series model for predicting monthly based municipal solid waste generation in Faridabad city of Haryana State (India) using artificial neural network (ANN) time series autoregressive approach. The collected municipal solid waste observations have been arranged monthly from 2010 to 2014. The 60 months data set is divided into 42 training data sets, 9 testing data sets and 9 validating data sets. Various structures of ANN have been investigated by changing the number of hidden layer neurons. Finally best optimized structure of neural network is found. The proposed model is validated by the minimum value of performance parameters such as mean square error 0.0003714, root mean square error 0.01927 and the high value of the coefficient of regression 0.8385. On the bases of these performance parameters it is concluded that the proposed ANN model gives accurate predictive results.
机译:摘要城市固体废物产生量的准确预测在未来规划和废物管理系统中具有重要作用。所产生的固体废物的特性在不同的地方(市政当局或国家/地区)不同。准确预测城市固体废物(MSW)的产生已成为现代时代的关键任务。其预测需要准确的MSW数据。本研究的目的是使用人工神经网络(ANN)时间序列自回归方法设计时间序列模型,以预测印度哈里亚纳邦法里达巴德市基于每月的城市固体废物产生量。从2010年到2014年,每月都会收集收集的城市固体废物观测数据。60个月的数据集分为42个培训数据集,9个测试数据集和9个验证数据集。通过改变隐层神经元的数量,研究了人工神经网络的各种结构。最终找到了神经网络的最佳优化结构。通过性能参数的最小值(例如均方误差0.0003714,均方根误差0.01927和回归系数的高值0.8385)验证了所提出的模型。基于这些性能参数,可以得出结论:所提出的人工神经网络模型给出了准确的预测结果。

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