A Fusion Water Quality Soft-Sensing Method Based on WASP Model and Its Application in Water Eutrophication Evaluation

Abstract

Water environment protection is of great significance for both economic development and improvement of people's livelihood, where modeling of water environment evolution is indispensable in water quality analysis. However, many water quality indexes related to water quality model cannot be measured online, and some model parameters always vary among different water areas. Thus, this paper proposes a water quality soft-sensing method based on the water quality mechanism model to simulate evolution of water quality indexes online, where unscented Kalman filter is utilized to estimate model parameters. Furthermore, a modified fuzzy comprehensive evaluation method is presented to evaluate the level of water eutrophication condition. Finally, the water quality data collected from Taihu Lake and Beihai Lake are used to validate the effectiveness and generality of the proposed method. The results show that the proposed soft-sensing method is able to describe the variation of related water quality indexes, with better accuracy compared to nonlinear least squares based method and traditional trial-and-error based method. On this basis, the water eutrophication condition can be also accurately evaluated.

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

“…Consideration of seasonal effects in WQ assessment is vital since WQ variables are often subjected to high variation in concentration based on the time of the year. Based on the papers reviewed, it is observed that four studies [22,95,97,100] did not report on the data collection interval used in building their predictive models. This points to the need for a more rigorous scientific reporting of data collection in data-driven modeling papers.…”

“…Additionally, a less complex predictive model with fewer features will require minimal resources to train and, therefore, will increase the model interpretability by the end-user [85]. Based on Table 7, two studies [97,100] did not give any detail concerning the data used, and therefore it is not clear whether it was processed or not. Seven studies pre-processed the data from the fourteen that provided some details while the other seven did not.…”

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

“…Currently, there are two main categories of methods for water environment prediction mechanism-driven methods and data-driven methods. Mechanism-driven models use differential or partial differential equations to describe the interactions of influencing factors, considering physical, chemical, and biological processes in aquatic ecosystems ( Wang et al, 2013 ; Wang et al, 2017 ; Wang et al, 2018a ; Woelmer et al, 2022 ; Yang et al, 2022 ). However, the mechanism-driven model does not have good generalization performance because of the different algal growth processes in different water bodies.…”

Fuzzy Evaluation of Water Quality Based on Micronucleus Technology of Vicia faba Root Tip

“…In recent years, the pollution status of water quality has been mostly based on conventional physical and chemical indicators, but it is hard to fully reflect the mixed effect of pollutants using the existing physical and chemical detection technology [1][2][3][4]. The plant genetic toxicity test can be widely used in the toxicological detection of important pollutants in water samples without knowing the chemical composition because of its short time consumption, low cost, and high sensitivity [5][6][7][8][9].…”