Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

Abstract

Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research.

Smart Aquaponics: An Automated Water Quality Management System for Sustainable Urban Agriculture

“…The mention of obtaining optimum values for each controller (vegetable tank, fish tank, and heater system) indicates a tailored approach to optimization. Earlier systems [19][20][21][22][23][24][25][26] may have employed generic control settings or overlooked the significance of fine-tuning control parameters for specific components within the aquaponic system. By optimizing the parameters for each subsystem, the proposed system is likely to achieve a superior overall performance and efficiency.…”

“…Research for managing water quality in treatment plants has intensified due to its substantial potential to enhance operational efficiency, ensure regulatory compliance, and improve overall water treatment effectiveness [16][17][18]. Simultaneously, research efforts [19][20][21][22][23][24][25][26][27][28][29][30] have concentrated on refining water management control. In the realm of aquaponics, where water plays a pivotal role, the research paper [29] offers a succinct overview of the proposed solutions in the literature.…”

“…In addition, they established a PID control system for optimizing the wastewater treatment process and ensuring system stability. This literature review [24][25][26][27][28] collectively serves as a fundamental foundation for our project, highlighting the importance of effective water management and the incorporation of advanced technologies to meet our goals of optimizing resource utilization and improving the stability of aquaponic systems. By drawing the insights gained from both studies, our goal is to create a holistic approach that integrates observed practices in water treatment into our aquaponics project.…”