Real-Time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation

Abstract

Deteriorating water quality leads to the freshwater biodiversity crisis. The interrelationships among water quality parameters and the relationships between these parameters and taxa groups are complicated in affecting biodiversity. Nevertheless, due to the limited types of Internet of Things (IoT) sensors available on the market, a large number of chemical and biological parameters still rely on laboratory tests. With the latest advancement in artificial intelligence and the IoT (AIoT), this technique can be applied to real-time monitoring of water quality, and further conserving biodiversity. In this paper, we conducted a comprehensive literature review on water quality parameters that impact the biodiversity of freshwater and identified the top-10 crucial water quality parameters. Among these parameters, the interrelationships between the IoT measurable parameters and IoT unmeasurable parameters are estimated using a general regression neural network model and a multivariate polynomial regression model based on historical water quality monitoring data. Conventional field water sampling and in-lab experiments, together with the developed IoT-based water quality monitoring system were jointly used to validate the estimation results along an urban river in Hong Kong. The general regression neural network model can successfully distinguish the abnormal increase of parameters against normal situations. For the multivariate polynomial regression model of degree eight, the coefficients of determination results are 0.89, 0.78, 0.87, and 0.81 for NO3-N, BOD5, PO4, and NH3-N, respectively. The effectiveness and efficiency of the proposed systems and models were validated against laboratory results and the overall performance is acceptable with most of the prediction errors smaller than 0.2mg/L, which provides insights into how AIoT techniques can be applied to pollutant discharge monitoring and other water quality regulatory applications for freshwater biodiversity conservation.

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

“…Transmitting environmental data online enables real-time monitoring, crucial for addressing issues such as deteriorating water quality and declining freshwater biodiversity [24]. The integration of IoT sensors allows continuous monitoring, providing insight into complex interrelationships among water quality parameters [25][26][27].…”

Insights on the internet of things: past, present, and future directions

“…Combining technologies such as IoT and AI helps businesses to stimulate innovation while remaining competitive. All it needs to get started is a notion or a vision for upgrading your company's infrastructure in order to confront the future head on [312], [313], [353]- [362].…”

“…Although machine learning is extremely adept at detecting patterns, it cannot do so in the absence of high-quality data. Machine learning will grow increasingly popular in the next years as bandwidth expands and IoT networks take center stage in a variety of industries [353], [363]- [380].…”

A Review of Efficient Real-Time Decision Making in the Internet of Things

“…In [51][52][53][54], GRNN (General Regression Neural Network) and GRNN-SGTM (GRNN-Successive Geometric Transformation Model) are used to recover missing IoT data, respectively. Wang et al [55] devise a GRNN and a multivariate polynomial regression model to estimate unmeasurable water quality parameters from measurable parameters. In addition, Tien [56] gives a high-level view of IoT, (near) real-time decision making, and artificial intelligence instead of focusing on technical approaches for real-time decision support in IoT, unlike this review.…”