Midwest Technology Assistance Center for Small Public Water Systems Final Report

The Midwest Technology Assistance Center (MTAC) was established October 1, 1998 to provide assistance to small public water systems throughout the Midwest via funding from the United States Environmental Protection Agency (USEPA) under section 1420(f) of the 1996 amendments to the Safe Drinking Water Act. This report summarizes progress made under USEPA Grant# 832591-01 for funds received in Federal Years (FY) 05 and 06. MTAC is a cooperative effort of the 10 states of the Midwest (congruent with USEPA regions 5 and 7), led by the Illinois State Water Survey and the University of Illinois. The director of their Water Resources Institute (WRI) coordinates the participation of each state in MTAC. Dr. Richard Warner (WRI director) and Kent Smothers were the principal investigators for this project. Kent Smothers serves as the managing director of the center, and is responsible for conducting routine activities with the advice and counsel of Dr. Richard Warner.published or submitted for publicationis peer reviewe

A Review and an Approach of Water Pollution Indication using Arduino Uno

Drinking water is critical for the wellbeing and prosperity of all people and creatures because water play major role in all living beings and most danger disease are caused by water and it is our duty to provide clean and safe water and also to monitor the pollution level in water it is additionally essential for farming utilization for good product yielding and natural way of life linkage wellbeing issues. With over 200 children dying per day due to unsafe water, drinking water crisis is ranked one on the global risk by World Economic Forum, 2015. This paper presents an easy and comprehensive methodology is microcontroller sensor based system continuous observing and pollution recognition for both drinking and non-drinking water dissemination frameworks and in addition for customer locales

ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems

This manuscript presents a novel state-of-the-art cyber-physical water testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA). ACWA is motivated by the need to advance water supply management using AI and Cybersecurity experimentation. The main goal of ACWA is to address pressing challenges in the water and agricultural domains by utilising cutting-edge AI and data-driven technologies. These challenges include Cyberbiosecurity, resources management, access to water, sustainability, and data-driven decision-making, among others. To address such issues, ACWA consists of multiple topologies, sensors, computational nodes, pumps, tanks, smart water devices, as well as databases and AI models that control the system. Moreover, we present ACWA simulator, which is a software-based water digital twin. The simulator runs on fluid and constituent transport principles that produce theoretical time series of a water distribution system. This creates a good validation point for comparing the theoretical approach with real-life results via the physical ACWA testbed. ACWA data are available to AI and water domain researchers and are hosted in an online public repository. In this paper, the system is introduced in detail and compared with existing water testbeds; additionally, example use-cases are described along with novel outcomes such as datasets, software, and AI-related scenarios

The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected objects, such as Radio-frequency identification (RFIDs), sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organisations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. More importantly, we identify the trends, opportunities and open challenges in the industry-based the IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: smart wearable, smart home, smart, city, smart environment, and smart enterprise. This survey is intended to serve as a guideline and conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201

A survey of machine learning methods applied to anomaly detection on drinking-water quality data

Abstract: Traditional machine learning (ML) techniques such as support vector machine, logistic regression, and artificial neural network have been applied most frequently in water quality anomaly detection tasks. This paper presents a review of progress and advances made in detecting anomalies in water quality data using ML techniques. The review encompasses both traditional ML and deep learning (DL) approaches. Our findings indicate that: 1) Generally, DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive rates. However, is difficult to make a fair comparison between studies because of different datasets, models and parameters employed. 2) We notice that despite advances made and the advantages of the extreme learning machine (ELM), application of ELM is sparsely exploited in this domain. This study also proposes a hybrid DL-ELM framework as a possible solution that could be investigated further and used to detect anomalies in water quality data