IoT in Water Quality Monitoring-Are We Really Here?

Reliable water quality prediction and parametric analysis using explainable AI models

The consumption of water constitutes the physical health of most of the living species and hence management of its purity and quality is extremely essential as contaminated water has to potential to create adverse health and environmental consequences. This creates the dire necessity to measure, control and monitor the quality of water. The primary contaminant present in water is Total Dissolved Solids (TDS), which is hard to filter out. There are various substances apart from mere solids such as potassium, sodium, chlorides, lead, nitrate, cadmium, arsenic and other pollutants. The proposed work aims to provide the automation of water quality estimation through Artificial Intelligence and uses Explainable Artificial Intelligence (XAI) for the explanation of the most significant parameters contributing towards the potability of water and the estimation of the impurities. XAI has the transparency and justifiability as a white-box model since the Machine Learning (ML) model is black-box and unable to describe the reasoning behind the ML classification. The proposed work uses various ML models such as Logistic Regression, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree (DT) and Random Forest (RF) to classify whether the water is drinkable. The various representations of XAI such as force plot, test patch, summary plot, dependency plot and decision plot generated in SHAPELY explainer explain the significant features, prediction score, feature importance and justification behind the water quality estimation. The RF classifier is selected for the explanation and yields optimum Accuracy and F1-Score of 0.9999, with Precision and Re-call of 0.9997 and 0.998 respectively. Thus, the work is an exploratory analysis of the estimation and management of water quality with indicators associated with their significance. This work is an emerging research at present with a vision of addressing the water quality for the future as well.

Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication

The increasing demand for water and worsening climate change place significant pressure on this vital resource, making its preservation a global priority. Water quality monitoring programs are essential for effectively managing this resource. Current programs rely on traditional monitoring approaches, leading to limitations such as low spatiotemporal resolution and high operational costs. Despite the adoption of novel monitoring approaches that enable better data resolution, the public's comprehension of water quality matters remains low, primarily due to communication process deficiencies. This study explores the advantages and challenges of using Internet of Things (IoT) and citizen science as alternative monitoring approaches, emphasizing the need for enhancing public communication of water quality data. Through a systematic review of studies implemented on-field, we identify and propose strategies to address five key challenges that IoT and citizen science monitoring approaches must overcome to mature into robust sources of water quality information. Additionally, we highlight three fundamental problems affecting the water quality communication process and outline strategies to convey this topic effectively to the public.

Sensors for Emerging Water Contaminants: Overcoming Roadblocks to Innovation

Ensuring water quality and safety requires the effective detection of emerging contaminants, which present significant risks to both human health and the environment. Field deployable low-cost sensors provide solutions to detect contaminants at their source and enable large-scale water quality monitoring and management. Unfortunately, the availability and utilization of such sensors remain limited. This Perspective examines current sensing technologies for detecting emerging contaminants and analyzes critical barriers, such as high costs, lack of reliability, difficulties in implementation in real-world settings, and lack of stakeholder involvement in sensor design. These technical and nontechnical barriers severely hinder progression from proof-of-concepts and negatively impact user experience factors such as ease-of-use and actionability using sensing data, ultimately affecting successful translation and widespread adoption of these technologies. We provide examples of specific sensing systems and explore key strategies to address the remaining scientific challenges that must be overcome to translate these technologies into the field such as improving sensitivity, selectivity, robustness, and performance in real-world water environments. Other critical aspects such as tailoring research to meet end-users' requirements, integrating cost considerations and consumer needs into the early prototype design, establishing standardized evaluation and validation protocols, fostering academia-industry collaborations, maximizing data value by establishing data sharing initiatives, and promoting workforce development are also discussed. The Perspective describes a set of guidelines for the development, translation, and implementation of water quality sensors to swiftly and accurately detect, analyze, track, and manage contamination.

An Intelligent Water Monitoring IoT System for Ecological Environment and Smart Cities

Global precipitation is becoming increasingly intense due to the extreme climate. Therefore, creating new technology to manage water resources is crucial. To create a sustainable urban and ecological environment, a water level and water quality control system implementing artificial intelligence is presented in this research. The proposed smart monitoring system consists of four sensors (two different liquid level sensors, a turbidity and pH sensor, and a water oxygen sensor), a control module (an MCU, a motor, a pump, and a drain), and a power and communication system (a solar panel, a battery, and a wireless communication module). The system focuses on low-cost Internet of Things (IoT) devices along with low power consumption and high precision. This proposal collects rainfall from the preceding 10 years in the application region as well as the region's meteorological bureau's weekly weather report and uses artificial intelligence to compute the appropriate water level. More importantly, the adoption of dynamic adjustment systems can reserve and modify water resources in the application region more efficiently. Compared to existing technologies, the measurement approach utilized in this study not only achieves cost savings exceeding 60% but also enhances water level measurement accuracy by over 15% through the successful implementation of water level calibration decisions utilizing multiple distinct sensors. Of greater significance, the dynamic adjustment systems proposed in this research offer the potential for conserving water resources by more than 15% in an effective manner. As a result, the adoption of this technology may efficiently reserve and distribute water resources for smart cities as well as reduce substantial losses caused by anomalous water resources, such as floods, droughts, and ecological concerns.

A Prototype for an Intelligent Water Management System for Household Use

Water scarcity is becoming an issue of more significant concern with a major impact on global sustainability. For it, new measures and approaches are urgently needed. Digital technologies and tools can play an essential role in improving the effectiveness and efficiency of current water management approaches. Therefore, a solution is proposed and validated, given the limited presence of models or technological architectures in the literature to support intelligent water management systems for domestic use. It is based on a layered architecture, fully designed to meet the needs of households and to do so through the adoption of technologies such as the Internet of Things and cloud computing. By developing a prototype and using it as a use case for testing purposes, we have concluded the positive impact of using such a solution. Considering this is a first contribution to overcome the problem, some issues will be addressed in a future work, namely, data and device security and energy and traffic optimisation issues, among several others.

Low-Cost Water Quality Sensors for IoT: A Systematic Review

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices.