Fault detection in water supply systems using hybrid (theory and data-driven) modelling

Data Analytics for Environmental Science and Engineering Research

The advent of new data acquisition and handling techniques has opened the door to alternative and more comprehensive approaches to environmental monitoring that will improve our capacity to understand and manage environmental systems. Researchers have recently begun using machine learning (ML) techniques to analyze complex environmental systems and their associated data. Herein, we provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications. We present current applications of ML algorithms within the ESE domain using three representative case studies: (1) Metagenomic data analysis for characterizing and tracking antimicrobial resistance in the environment; (2) Nontarget analysis for environmental pollutant profiling; and (3) Detection of anomalies in continuous data generated by engineered water systems. We conclude by proposing a path to advance incorporation of data analytics approaches in ESE research and application.

Water Leak Localization Using High-Resolution Pressure Sensors

A new method for identifying a leaking pipe within a pressurized water distribution system is presented. This novel approach utilizes transient modeling to analyze water networks. Urban water supply networks are important infrastructure that ensures the daily water consumption of urban residents and industrial sites. The aging and deterioration of drinking water mains is the cause of frequent burst pipes, thus making the detection and localization of these bursts a top priority for water distribution companies. Here we describe a novel method based on transient modeling of the water network and produces high-resolution pressure response under various scenarios. Analyzing this data allows the prediction of the leaking pipe. The transient pressure data is classified as leaking pipes or no leak clusters using the K-nearest neighbors (K-NN) algorithm. The transient model requires a massive computation effort to simulate the network’s performance. The classification model presented good performance with an overall accuracy of 0.9 for the basic scenarios. The lowest accuracy was obtained for interpolated scenarios the model had not been trained on; in this case, the accuracy was 0.52.

Edge analytics for anomaly detection in water networks by an Arduino101-LoRa based WSN

This paper presents a novel distributed data analytic architecture, and corresponding algorithms that apply to infrastructure anomaly detection. The proposed method mainly focuses on smart water networks, demonstrating that the highest possible sensor rate analytic performs at on-edge nodes without requiring the whole date to send back to the server. This approach saves communication costs and lengthens the lifetime of the battery-powered nodes. A complex set of tasks is developed on a single-core Intel Curie processor, Arduino101 and the raw sensor data is compressed using a customized Lempel-Ziv compression algorithm tailored to resource-constrained embedded systems. The compression rate figures are then analyzed but only the compressed data which is associated with the anomalous condition is sent back to the server by means of a LoRa platform. The developed system is evaluated experimentally and the results verify the high resource utilization.

Urban Comprehensive Water Consumption: Nonlinear Control of Production Factor Input Based upon the C-D Function

Utilizing the urban water demand function and the Cobb-Douglas (C-D) production function, an economic control model for the multi-input-multi-output (MIMO) nonlinear system was designed and implemented to describe urban comprehensive water consumption, where the urban water demand function was expressed as the product of the number of water users and per capita comprehensive water consumption, and the urban water supply function was expressed as a C-D production function. The control variables included capital investment and labor input for the urban water supply. In contrast to the Solow model, Shell model and aggregate model with renewable labor resources, the proposed model eliminated value constraints on investment and labor input in the state equations and hence avoided the difficulty in applying these models to urban water supply institutions. Furthermore, the feedback linearization control design (FLCD) method was employed to accomplish stability of the system. In contrast to the optimal control method, the FLCD method possesses an explicit solution of the control law and does not require the solution of a two-point boundary value problem of an ordinary differential equation, making the method more convenient for application. Moreover, two different scenarios of urban water consumption, one for the growth period and the other for the decline period, were simulated to demonstrate the effectiveness of the proposed control scheme.

Approaches to Failure Risk Analysis of the Water Distribution Network with Regard to the Safety of Consumers

Contemporary risk assessment makes reference to current world trends, whereby there is increased emphasis on safety. This paper has thus sought mainly to present new approaches to failure risk assessment where the functioning of a water distribution network (WDN) is concerned. The framework for the research involved here has comprised of: (a) an analysis of WDN failure in regard to an urban agglomeration in south-east Poland; (b) failure rate analysis, taking account of the type of a water pipe (mains, distribution, service connections (SC)) and months of the year, with an assessment of results in terms of criterion values for failure rate; (c) a determination—by reference to analyses performed previously—of the compatibility of experts’ assessments in terms of standards of failure and obtained results, through rank analysis; and (d) the proposing of a modified Multi-Criteria Decision Analysis with implementation of an Analytical Hierarchy Process, to allow failure risk assessment for the WDN to be performed, on the basis of the calculated additive value of obtained risk. The analysis in question was based on real operating data, as collected from the water distribution company. It deals with failures in the WDN over a period of 13 years in operation, from 2005 to 2017.