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

Abstract

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.

Water, Resources, and Resilience: Insights from Diverse Environmental Studies

“…By leveraging AI's capabilities, researchers can develop predictive models, optimize resource allocation, and enhance decision-making processes. AI-driven monitoring systems can provide real-time insights into water quality, usage patterns, and network vulnerabilities, thereby promoting resilience and sustainability [64][65][66][67][68][69][70][71][72][73].…”

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

“…Further complicating matters is the transformation of many of these natural streams into closed pipe systems within cities [32]. While this may serve immediate urban planning needs, it often results in a hydraulic capacity that is insufficient for handling the large volumes of stormwater brought on by heavy downpours [33,34]. The challenge is compounded by the prevalence of impervious surfaces in urban areas, such as concrete and asphalt.…”