Efficient Water Quality Prediction Using Supervised Machine Learning
Efficient Water Quality Prediction Using Supervised Machine Learning
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DOI：
10.3390/w11112210
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发表时间：
2019-11-01
期刊：
影响因子：
3.4
通讯作者：
Garcia-Nieto, Jose
中科院分区：
文献类型：
--
作者：
Ahmed, Umair;Mumtaz, Rafia;Garcia-Nieto, Jose
水约占地球表面的70%，是维持生命的最重要来源之一。快速的城市化和工业化导致水质以惊人的速度恶化，导致令人痛心的疾病。传统上，通过昂贵且耗时的实验室和统计分析来估计水质，这使得实时监测的当代概念毫无意义。水质差的惊人后果需要一种更快、更便宜的替代方法。基于这一动机，本研究探索了一系列有监督的机器学习算法来估计水质指数（WQI）和水质类（WQC），水质指数是描述水的总体质量的奇异指数，水质类是基于WQI定义的独特类。所提出的方法采用四个输入参数，即温度，浊度，pH值和总溶解固体。在所有采用的算法中，学习率为0.1的梯度提升和度为2的多项式回归最有效地预测WQI，分别具有1.9642和2.7273的平均绝对误差（MAE）。而多层感知器（MLP），具有（3，7）的配置，分类WQC最有效，精度为0.8507。所提出的方法达到合理的精度，使用最少数量的参数，以验证其在真实的时间水质检测系统中使用的可能性。
Water makes up about 70% of the earth's surface and is one of the most important sources vital to sustaining life. Rapid urbanization and industrialization have led to a deterioration of water quality at an alarming rate, resulting in harrowing diseases. Water quality has been conventionally estimated through expensive and time-consuming lab and statistical analyses, which render the contemporary notion of real-time monitoring moot. The alarming consequences of poor water quality necessitate an alternative method, which is quicker and inexpensive. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality index (WQI), which is a singular index to describe the general quality of water, and the water quality class (WQC), which is a distinctive class defined on the basis of the WQI. The proposed methodology employs four input parameters, namely, temperature, turbidity, pH and total dissolved solids. Of all the employed algorithms, gradient boosting, with a learning rate of 0.1 and polynomial regression, with a degree of 2, predict the WQI most efficiently, having a mean absolute error (MAE) of 1.9642 and 2.7273, respectively. Whereas multi-layer perceptron (MLP), with a configuration of (3, 7), classifies the WQC most efficiently, with an accuracy of 0.8507. The proposed methodology achieves reasonable accuracy using a minimal number of parameters to validate the possibility of its use in real time water quality detection systems.