Detecting anomalies in water distribution networks using EPR modelling paradigm

This is the author accepted manuscript. The final version is available from IWA Publishing via the DOI in this record.Sustainable management of water distribution networks (WDNs) requires effective exploitation of available data from pressure/flow devices. Water companies collect a large amount of such data, which need to be managed correctly and analysed effectively using appropriate techniques. Furthermore, water companies need to balance the data gathering and handling costs with the benefits of extracting useful information. Recent approaches implementing data mining techniques for analysing pressure/flow data appear very promising, because they can automate mundane tasks involved in data analysis process and efficiently deal with sensor data collected. Furthermore, they rely on empirical observations of a WDN behaviour over time, allowing reproducing/predicting possible future behaviour of the network. This paper investigates the effectiveness of the evolutionary polynomial regression (EPR) paradigm to reproduce the behaviour of a WDN using online data recorded by low-cost pressure/flow devices. Using data from a real district metered area, the case study presented shows that by using the EPR paradigm a model can be built which enables the accurate reproduction and prediction of the WDN behaviour over time and detection of flow anomalies due to possible unreported bursts or unknown increase of water withdrawal. Such an EPR model might be integrated into an early warning system to raise alarms when anomalies are detected.The research reported in this paper was founded by two projects of the Italian Scientific Research Program of National Interest PRIN-2012: ‘Analysis tools for management of water losses in urban aqueducts’ and ‘Tools and procedures for advanced and sustainable management of water distribution networks’

Modeling of local scour depth downstream hydraulic structures in trapezoidal channel using GEP and ANNs

AbstractLocal scour downstream stilling basins is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour depth. Lack estimation of local scour can endanger to stability of hydraulic structure and can cause risk of failure. This paper presents Gene expression program (GEP) and artificial neural network (ANNs), to simulate local scour depth downstream hydraulic structures. The experimental data is collected from the literature for the scour depth downstream the stilling basin through a trapezoidal channel. Using GEP approach gives satisfactory results compared with artificial neural network (ANN) and multiple linear regression (MLR) modeling in predicting the scour depth downstream of hydraulic structures

Data-mining approach to investigate sedimentation features in combined sewer overflows

Sedimentation is the most common and effectively practiced method of urban drainage control in terms of operating installations and duration of service. Assessing the percentage of suspended solids removed after a given detention time is essential for both design and management purposes. In previous experimental studies by some of the authors, the expression of iso-removal curves (i.e. representing the water depth where a given percentage of suspended solids is removed after a given detention time in a sedimentation column) has been demonstrated to depend on two parameters which describe particle settling velocity and flocculation factor. This study proposes an investigation of the influence of some hydrological and pollutant aggregate information of the sampled events on both parameters. The Multi-Objective (EPR-MOGA) and Multi-Case Strategy (MCS-EPR) variants of the Evolutionary Polynomial Regression (EPR) are originally used as data-mining strategies. Results are proved to be consistent with previous findings in the field and some indications are drawn for relevant practical applicability and future studies

Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm

Radial Basis Function Neural Network (RBFNN) is a variant of artificial neural network (ANN) paradigm, utilized in a plethora of fields of studies such as engineering, technology and science. 2 Satisfiability (2SAT) programming has been coined as a prominent logical rule that defines the identity of RBFNN. In this research, a swarm-based searching algorithm namely, the Artificial Bee Colony (ABC) will be introduced to facilitate the training of RBFNN. Worth mentioning that ABC is a new population-based metaheuristics algorithm inspired by the intelligent comportment of the honey bee hives. The optimization pattern in ABC was found fruitful in RBFNN since ABC reduces the complexity of the RBFNN in optimizing important parameters. The effectiveness of ABC in RBFNN has been examined in terms of various performance evaluations. Therefore, the simulation has proved that the ABC complied efficiently in tandem with the Radial Basis Neural Network with 2SAT according to various evaluations such as the Root Mean Square Error (RMSE), Sum of Squares Error (SSE), Mean Absolute Percentage Error (MAPE), and CPU Time. Overall, the experimental results have demonstrated the capability of ABC in enhancing the learning phase of RBFNN-2SAT as compared to the Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm

Optimal Design of District Metering Areas

Abstract The search for optimal segmentations aimed at defining district metering areas (DMAs) is a challenging and crucial issue in the analysis, planning and management of water distribution networks (WDNs). The need to select optimal segmentations relates to a number of important technical reasons. Today, the most relevant one is the leakage management by means of pressure-control zones. This contribution proposes a novel two-steps strategy for DMAs planning. The strategy is based on the segmentation design as first step, to achieve a scenario of optimal locations of "conceptual cuts"; during the second step, these are the candidate for the location of (closed) gate valves or flow measurement devices that gave rise to district monitoring areas (DMAs). The segmentation step is performed solving a multi-objective optimization problem (i.e. WDN-oriented modularity maximization versus the number of "conceptual cuts" minimization). The second step accomplishes the real DMAs design by solving a three-objective optimization, i.e. the minimization of the background leakages versus the unsupplied customers demand versus the flow observations. This means that the procedure will search for a set of scenarios having a number of closed gate valves installed at the "conceptual cuts" that do not decrease the WDN hydraulic capacity below that necessary for a sufficient service to customers, while contemporarily reducing the background leakages. A pressure-driven modelling approach is used to predict background leakage reduction and the unsupplied customers demand. The procedure is explained on a benchmark network from literature, the Apulian network

INPUT SELECTION BY EPR-MOGA

The growing availability of field data, from information and communication technologies (ICTs) in "smart'' urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multi-objective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure

An Integrated Modeling Approach to Optimize the Management of a Water Distribution System: Improving the Sustainability While Dealing with Water Loss, Energy Consumption and Environmental Impacts

Research article There is a strong link between water and energy in municipal water systems then the Alliance to Save Energy coined the term "Watergy" [1]. Each component of the integrated water system contributes differently to the energy balance. With regard to urban water distribution systems (WDS), the pumping energy cost represents the single largest part of the total operational cost, also magnified by every litre of water lost to leaks. Even a small increase in operational efficiency may result in significant cost savings to the water industries. Therefore the inefficient management of water distribution systems results not only into depletion of water resources but also into energy consumption that increase CO2 emissions related also to the treatment of water volumes greater than needed, with use of excessive chemical components and consequent higher environmental global impact. The research outlined in this contribution was born with the aim to develop appropriate methodologies and tools to support the optimization of the WDS performance, in terms of water saving and reduction of energy consumptions and consequently environmental impacts. The integration of advanced WDS hydraulic modelling with a material and energy flow analysis is proposed herein, where the output of the hydraulic simulations permits to get more accurate input for a metabolic analysis of the system Next phases of this research will test the integrated model under different scenarios, aimed at quantifying the environmental impact of different WDS management solutions by means of selected indicator

An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

Developing empirical formulae for scour depth in front of Inclined bridge piers

Neka istraživanja predlažu različite empirijske korelacije kako bi se predvidjela dubina podlokavanja ispred nagnutih stupova mosta kroz regresijsku analizu dobivenu laboratorijskim mjerenjima zbog složenih mehanizama toka oko nagnutih stupova mosta. Međutim, kako su se te korelacije razvile za određeni skup podataka, opća je jednadžba i dalje potrebna da bi se točno predvidjela dubina podlokavanja ispred nagnutih stupova mosta. Glavni je cilj istraživanja razviti opću jednadžbu kako bi se predvidjela dubina podlokavanja ispred nagnutih stupova mosta kroz višeslojni perceptron (MLP) i tehnike neuronske mreže s radijalnim baznim funkcijama (RBNN). Eksperimentalni skupovi podataka koji se primjenjuju u ovom istraživanju skupljeni su se iz prijašnjih istraživanja. Jednadžba za dubinu podlokavanja prednjeg stupa koristi se primjenom pet varijabl. Rezultati analiza umjetne neuronske mreže (ANN) otkrivaju da su modeli RBNN i MLP omogućili preciznija predviđanja nego prethodne empirijske korelacije kad su u pitanju izlazne varijable. Prema tome, predlažu se analitičke jednadžbe dobivene RBNN i MLP modelima za točno predviđanje dubine podlokavanja ispred nagnutih stupova mosta. Štoviše, na temelju rezultata analize osjetljivosti utvrđuje se da je na dubinu podlokavanja ispred prednjih i stražnjih stupova najviše utjecao kut nagiba, odnosno intenzitet toka.Because of the complex flow mechanism around inclined bridge piers, previous studies have proposed different empirical correlations to predict the scouring depth in front of piers, which include regression analysis developed from laboratory measurements. However, because these correlations were developed for particular datasets, a general equation is still required to accurately predict the scour depth in front of inclined bridge piers. The aim of this study is to develop a general equation to predict the local scour depth in front of inclined bridge pier systems using multilayer perceptron (MLP) and radial-basis neural-network (RBNN) techniques. The experimental datasets used in this study were obtained from previous research. The equation for the scour depth of the front pier was developed using five variables. The results of the artificial neural-network (ANN) analyses revealed that the RBNN and MLP models provided more accurate predictions than the previous empirical correlations for the output variables. Accordingly, analytical equations derived from the RBNN and MLP models were proposed to accurately predict the scouring depth in front of inclined bridge piers. Moreover, from the sensitivity analyses results, we determined that the scour depths in front of the front and back piers were primarily influenced by the inclination angle and flow intensity, respectively