利用数据采掘技术估算尼日利亚拉各斯的部门污染负荷
Estimating Sectoral Pollution Load in Lagos, Nigeria Using Data Mining Techniques
摘要：
工业污染通常被认为是造成空气、水和土壤污染的主要因素之一。使用工业污染预测系统估算了进入拉各斯不同介质（即空气、水和土地）的部门污染负荷（吨/年）。使用人工神经网络（ANN）对这些进行了进一步研究，人工神经网络是一种数据采掘技术，具有在具有非线性相关变量的大型数据集中检测和描述模式的能力。在多层感知器网络、广义前馈神经网络、径向基函数网络和递归网络中，时滞递归网络（TLRN）是最好的神经网络模型。TLRN模型的平均误差（MAE）（0.14），时间（39秒）和线性相关系数（0.84）方面的数据集优于其他。结果表明，人工神经网络（ANN）技术（即，时滞递归网络模型在环境评价研究中也是适用和有效的。保留字：人工神经网络，数据采掘技术，工业污染预测系统，污染负荷，污染强度。
英文摘要：
Industrial pollution is often considered to be one of the prime factors contributing to air, water and soil pollution. Sectoral pollution loads (ton/yr) into different media (i.e. air, water and land) in Lagos were estimated using Industrial Pollution Projected System (IPPS). These were further studied using Artificial neural Networks (ANNs), a data mining technique that has the ability of detecting and describing patterns in large data sets with variables that are non- linearly related. Time Lagged Recurrent Network (TLRN) appeared as the best Neural Network model among all the neural networks considered which includes Multilayer Perceptron (MLP) Network, Generalized Feed Forward Neural Network (GFNN), Radial Basis Function (RBF) Network and Recurrent Network (RN). TLRN modelled the data-sets better than the others in terms of the mean average error (MAE) (0.14), time (39 s) and linear correlation coefficient (0.84). The results showed that Artificial Neural Networks (ANNs) technique (i.e., Time Lagged Recurrent Network) is also applicable and effective in environmental assessment study. Keywords: Artificial Neural Networks (ANNs), Data Mining Techniques, Industrial Pollution Projection System (IPPS), Pollution load, Pollution Intensity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
