Prediction of Physico-Chemical Parameters of Surface Waters Using Autoregressive Moving Average Models: A Case Study of Kis-Balaton Water Protection System, Hungary

Author:

Kovács Zsófia12ORCID,Tarcsay Bálint Levente23ORCID,Tóth Piroska12ORCID,Juhász Csenge Judit23,Németh Sándor3ORCID,Shahrokhi Amin4ORCID

Affiliation:

1. Sustainability Solutions Research Laboratory, Research Centre for Biochemical, Environmental and Chemical Engineering, University of Pannonia, 8200 Veszprém, Hungary

2. National Laboratory for Water Science and Water Security, University of Pannonia, 8200 Veszprém, Hungary

3. Department of Process Engineering, Research Centre for Biochemical, Environmental and Chemical Engineering, University of Pannonia, 8200 Veszprém, Hungary

4. Department of Radiochemistry and Radioecology, Research Centre for Biochemical, Environmental and Chemical Engineering, University of Pannonia, 8200 Veszprém, Hungary

Abstract

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3).

Funder

Széchenyi Plan Plus

Publisher

MDPI AG

Reference37 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3