论文标题
使用随机森林预测污染水平：菲律宾Bulacan省Marilao河的案例研究
Predicting Pollution Level Using Random Forest: A Case Study of Marilao River in Bulacan Province, Philippines
论文作者
论文摘要
这项研究旨在预测威胁菲律宾Bulacan省的Marilao河的污染水平。该地区的居民现在正受到污染。该水路的污染来自正式和非正式行业，例如二手铅电池，开放式垃圾场金属炼油和其他有毒金属。使用各种水质参数，例如溶解氧（DO），氢（pH）的潜力，生化氧需求（BOD）和总悬浮固体（TSS）是预测污染水平的基础。这项研究使用了基于2013年1月至2017年11月收集的样本数据的数据挖掘技术。这些数据被用作训练数据和测试结果，以预测河流状况，其相应的污染水平分类与颜色相应的污染水平分类，例如绿色的颜色，例如正常的绿色，平均污染和红色的橙色和红色污染的橙色。该模型的精度为91.75％，KAPPA值为0.8115，在一致性水平方面被解释为强大。
This study aims to predict the pollution level that threatens the Marilao River, located in the province of Bulacan, Philippines. The inhabitants of this area are now being exposed to pollution. Contamination of this waterway comes from both formal and informal industries, such as a used lead-acid battery, open dumpsites metal refining, and other toxic metals. Using various water quality parameters like Dissolved Oxygen (DO), Potential of Hydrogen (pH), Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSS) were the basis for predicting the pollution level. This study used the Data Mining technique based on the sample data collected from January of 2013 to November of 2017. These were used as a training data and test results to predict the river condition with its corresponding pollution level classification indicated with the used of colors such as Green for Normal, Yellow for Average, Orange for Polluted and Red for Highly Polluted. The model got an accuracy of 91.75% with a Kappa value of 0.8115, interpreted as Strong in terms of the level of agreement.
