Water Quality Monitoring Using ML Algorithms In Cloud-enabled LoRaWAN
Disciplines
Data Science | Other Computer Sciences | Programming Languages and Compilers | Systems Architecture | Theory and Algorithms
Abstract (300 words maximum)
Water, as one of Earth's most precious resources, is indispensable for the survival and prosperity of all living organisms. It is a fundamental component of ecosystems and a critical element of our daily lives. Monitoring the quality of water plays a significant role in environmental and resource management efforts. Likewise, ensuring safe and clean drinking water is vital, as contaminated water can carry disease-causing microorganisms, chemicals, and toxins that pose health risks when consumed or exposed to the skin. Unfortunately, the effectiveness of traditional water quality monitoring systems has often been hindered by challenges such as high operational costs, sporadic data collection, and delayed response to contamination events. To address these limitations, this paper delves into the integration of Machine Learning (ML) algorithms into Water Quality Monitoring using Cloud-enabled Long Range Wide Area networks (LoRaWAN). ML contributes invaluable data analysis capabilities, including anomaly detection, predictive modeling, and adaptive monitoring strategies. One of the most significant impacts of ML on water quality monitoring is its ability to improve the efficiency and accuracy of data analysis. ML algorithms can be trained on large datasets of historical water quality data to identify patterns and trends. This information can then be used to develop predictive models that can forecast future water quality conditions. This is particularly valuable for detecting and responding to contamination events early on. The integration of ML into water quality monitoring has had a significant impact on the field of environmental protection and water resource management. ML-enabled monitoring systems are helping to improve the efficiency, accuracy, and accessibility of water quality monitoring, while also reducing costs. This is leading to better decision-making and more effective water management practices.
Academic department under which the project should be listed
CCSE - Computer Science
Primary Investigator (PI) Name
Ahyoung Lee
Additional Faculty
Amy Gruss, SPCEET - Civil and Environmental Engineering, agruss@kennesaw.edu
Michael Beach, CSM - Molecular and Cellular Biology, mbeach2@kennesaw.edu
Water Quality Monitoring Using ML Algorithms In Cloud-enabled LoRaWAN
Water, as one of Earth's most precious resources, is indispensable for the survival and prosperity of all living organisms. It is a fundamental component of ecosystems and a critical element of our daily lives. Monitoring the quality of water plays a significant role in environmental and resource management efforts. Likewise, ensuring safe and clean drinking water is vital, as contaminated water can carry disease-causing microorganisms, chemicals, and toxins that pose health risks when consumed or exposed to the skin. Unfortunately, the effectiveness of traditional water quality monitoring systems has often been hindered by challenges such as high operational costs, sporadic data collection, and delayed response to contamination events. To address these limitations, this paper delves into the integration of Machine Learning (ML) algorithms into Water Quality Monitoring using Cloud-enabled Long Range Wide Area networks (LoRaWAN). ML contributes invaluable data analysis capabilities, including anomaly detection, predictive modeling, and adaptive monitoring strategies. One of the most significant impacts of ML on water quality monitoring is its ability to improve the efficiency and accuracy of data analysis. ML algorithms can be trained on large datasets of historical water quality data to identify patterns and trends. This information can then be used to develop predictive models that can forecast future water quality conditions. This is particularly valuable for detecting and responding to contamination events early on. The integration of ML into water quality monitoring has had a significant impact on the field of environmental protection and water resource management. ML-enabled monitoring systems are helping to improve the efficiency, accuracy, and accessibility of water quality monitoring, while also reducing costs. This is leading to better decision-making and more effective water management practices.