Water resources are not only the material basis to support the current social and economic development, but also an important resource to maintain people’s daily life. Therefore, the protection of water resources from being infringed not only requires the cooperation of relevant researchers, but also is closely related to every resident in the society. Everyone needs to pay more attention to it. In the current society, the water environment in different regions is generally protected through multiple processes. Among them, the first line of defense is the monitoring of water quality and the prediction model of indicators. The monitoring and early warning model of water quality indicators is generally based on the real-time monitoring of the content of various substances in the water environment, so as to comprehensively evaluate the water environment and predict the water quality in the future period according to the concentration of different substances. The existing water quality indicator monitoring and prediction model not only is an important technology to control water environmental pollution in the current society, but also can reflect the quality status in the target waters and the future water quality development trend in all aspects. More professional water quality related data can be collected through the existing monitoring models of various indicators of water quality. Based on the in-depth analysis of these data, the main pollutants in the target waters, the current pollution situation and the possible future damage can be fully understood. On the other hand, this water quality indicator monitoring model has also provided assistance for professionals to develop more scientific and reasonable water pollution control plans. In this paper, the existing water quality index monitoring and prediction model was updated by using the stochastic forest algorithm model. The stochastic forest algorithm model generally predicts the indicators and pollution status of the target water area in the future by processing the relevant data of the water quality indicators of the target water area. Finally, the performance of this new water quality monitoring index and prediction model has been improved by about 26% on average.

Erika Ottaviano. Prediction of Water Quality Monitoring Indicators Based on Random Forest. Water Pollution Prevention and Control Project (2020), Vol. 1, Issue 2: 11-19. https://doi.org/10.38007/WPPCP.2020.010202.

[1] Solangi Ghulam Shabir. Groundwater quality evaluation using the water quality index (WQI), the synthetic pollution index (SPI), and geospatial tools: a case study of Sujawal district, Pakistan. Human and Ecological Risk Assessment: An International Journal. (2020) 26(6): 1529-1549. https://doi.org/10.1080/10807039.2019.1588099

[2] Alizadeh Mohamad Javad. Effect of river flow on the quality of estuarine and coastal waters using machine learning models. Engineering Applications of Computational Fluid Mechanics. (2018) 12(1): 810-823. https://doi.org/10.1080/19942060.2018.1528480

[3] Zhi Wei. From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? Environmental science & technology. (2020) 55(4): 2357-2368. https://doi.org/10.1021/acs.est.0c06783

[4] Camara Moriken, Nor Rohaizah Jamil, Ahmad Fikri Bin Abdullah. Impact of land uses on water quality in Malaysia: a review. Ecological Processes. (2019) 8(1): 1-10. https://doi.org/10.1186/s13717-019-0164-x

[5] Son Cao Truong. Assessment of Cau River water quality assessment using a combination of water quality and pollution indices. Journal of Water Supply: Research and Technology-Aqua. (2020) 69(2): 160-172. https://doi.org/10.2166/aqua.2020.122

[6] Hamid Aadil, Sami Ullah Bhat, Arshid Jehangir. Local determinants influencing stream water quality. Applied Water Science. (2020) 10(1): 1-16. https://doi.org/10.1007/s13201-019-1043-4

[7] Muharemi Fitore, Florin Leon. Machine learning approaches for anomaly detection of water quality on a real-world data set. Journal of Information and Telecommunication. (2019) 3(3): 294-307. https://doi.org/10.1080/24751839.2019.1565653

[8] Bisht Anil Kumar. Artificial neural network based water quality forecasting model for ganga river. International Journal of Engineering and Advanced Technology. (2019) 8(6): 2778-2785. https://doi.org/10.35940/ijeat.F8841.088619

[9] HuanhaiYang, Shue Liu. A prediction model of aquaculture water quality based on multiscale decomposition. Mathematical Biosciences and Engineering. (2020) 18(6): 7561-7579. 

[10] Haghiabi Amir Hamzeh, Ali Heidar Nasrolahi, Abbas Parsaie. Water quality prediction using machine learning methods. Water Quality Research Journal. (2018) 53(1): 3-13. https://doi.org/10.2166/wqrj.2018.025

[11] Barzegar Rahim, Mohammad Taghi Aalami, Jan Adamowski. Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model. Stochastic Environmental Research and Risk Assessment. (2020) 34(2): 415-433. https://doi.org/10.1007/s00477-020-01776-2 

[12] Hassan Md Mehedi. Efficient prediction of water quality index (WQI) using machine learning algorithms. Human-Centric Intelligent Systems. (2020) 1(3-4): 86-97. https://doi.org/10.2991/hcis.k.211203.001 

[13] Ahmed Umair. Water quality monitoring: from conventional to emerging technologies. Water Supply. (2020) 20(1): 28-45. https://doi.org/10.2166/ws.2019.144

[14] Resende, Paulo Angelo Alves, Andre Costa Drummond. A survey of random forest based methods for intrusion detection systems. ACM Computing Surveys (CSUR). (2018) 51(3): 1-36. https://doi.org/10.1145/3178582

[15] ]Schonlau Matthias, Rosie Yuyan Zou. The random forest algorithm for statistical learning. The Stata Journal. (2020) 20(1): 3-29. https://doi.org/10.1177/1536867X20909688