Prediksi Kualitas Air Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA)

Ayu Agustin, Faisal Fajri Rahani, F. I. Indikawati
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引用次数: 1

Abstract

Water conservation is very necessary to support the creation of clean water quality that is free from harmful substances that can disturb the environment. So a system is needed to monitor water quality to determine the level of pollution that occurs. This system will work to see water quality in real time with several quality parameters such as pH, temperature, and water turbidity. The purpose of this research is to produce a predictive model and find out the prediction results of a data mining-based system. The method used to predict water quality uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) method, because the water quality data is thought to contain seasonal patterns. The results of this study indicate that the SARIMA model can be applied to the dataset used and obtain the accuracy of the forecasting results on each of the tested parameter data. The results of water quality forecasting with this parameter are the result data for testing at a dataset of a depth of 30 cm and a depth of 60 cm for temperature parameters, namely MSE<0.1, and RMSE<0.02. For pH parameters, MSE<0.1, and RMSE<0.1. As well as the turbidity parameter, the results of MSE<0.02, and RMSE<0.13. From these results indicate that this system can predict water quality with past data.
Prediksi Kualitas Air孟古纳坎方法季节性自回归综合移动平均(SARIMA)
节约用水是非常必要的,以支持创造干净的水质,没有有害物质,可以扰乱环境。因此,需要一个系统来监测水质,以确定发生的污染程度。该系统将通过几个质量参数(如pH值、温度和水浊度)实时查看水质。本研究的目的是建立一个基于数据挖掘系统的预测模型,并找出预测结果。用于预测水质的方法使用季节自回归综合移动平均(SARIMA)方法,因为水质数据被认为包含季节模式。研究结果表明,SARIMA模型可以应用于所使用的数据集,并在每个测试参数数据上获得预测结果的准确性。该参数的水质预报结果为温度参数在30 cm深度和60 cm深度数据集测试的结果数据,即MSE<0.1, RMSE<0.02。对于pH参数,MSE<0.1, RMSE<0.1。浊度参数,结果MSE<0.02, RMSE<0.13。结果表明,该系统能较好地利用以往数据进行水质预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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