Bulacan Hagonoy圣家堂五类固体废物产生的人工神经网络研究

John Mark Cagurungan, Royvin Factuar, Jan Marynelle Reyes, Dayanara Torres, Mark Paolo D. Mission, Florante D. Poso, Villamor D. Abad, Jon Arnel S. Telan
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引用次数: 3

摘要

固体废物的产生是世界上最普遍的挑战之一,特别是在人口拥挤和固体废物处理战略不充分的地方。有几个现存的影响固体废物产生的变量。在这方面,研究人员将重点放在对固体垃圾产生贡献最大的五(5)个元素或类别上。研究人员试图确定在这五种类型中,哪一种对圣家村的固体废物产生影响最大。这将有助于他们未来的固体废物管理计划,通过减少,分类和回收固体废物,这是他们的洪水问题的原因之一。人工神经网络是一种简化的计算脑模型,是固体废物管理中最常用的人工智能之一。为了获得预期的结果,矩阵实验室(MATLAB)测试是必不可少的。研究人员从该领域的研究、理论和文献中收集信息。研究人员随后进行了一项调查,以收集数据和现有数据,并使用Excel和矩阵实验室(MATLAB)构建神经网络分析的模型。最后对神经网络进行分析,目标值根据皮尔逊相关系数(Pearson’s Correlation Coefficient, R)变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Neural Network on Solid Waste Generation Based on Five (5) Categories Within Barangay Sagrada Familia in Hagonoy, Bulacan
Solid waste generation is one of the world’s most prevalent challenges, especially in places with crowded populations and inadequate solid waste disposal strategies. There are several extant influencing variables on solid waste creation. In this regard, the researchers focus on five (5) elements or categories that contributed the most to solid trash generation. The researchers sought to determine which one has the greatest influence on solid waste generation in Barangay Sagrada Familia among these five categories. This will contribute to their future solid waste management plan through minimizing, segregating, and recycling the solid waste, which is one of the causes of their flooding problem. ANN (Artificial Neural Network) is a simplified computational brain model that is one of the most often utilized artificial intelligence in solid waste management. To get the desired outcomes, Matrix Laboratory (MATLAB) testing is essential. The researchers gathered information from studies, theories, and literature in the field. The researchers then performed a survey to gather data and existing data in the barangay and used Excel and Matrix Laboratory (MATLAB) to construct the model for a Neural Network analysis. Finally, the authors analyzed the Neural Network, with the goal value varying according to Pearson’s Correlation Coefficient (R).
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