Artificial Neural Network Model for a Low Cost Failure Sensor: Performance Assessment in Pipeline Distribution

YesThis paper describes an automated event detection and location system for water distribution pipelines which is based upon low-cost sensor technology and signature analysis by an Artificial Neural Network (ANN). The development of a low cost failure sensor which measures the opacity or cloudiness of the local water flow has been designed, developed and validated, and an ANN based system is then described which uses time series data produced by sensors to construct an empirical model for time series prediction and classification of events. These two components have been installed, tested and verified in an experimental site in a UK water distribution system. Verification of the system has been achieved from a series of simulated burst trials which have provided real data sets. It is concluded that the system has potential in water distribution network management

Validation and reconstruction of flow meter data in the Barcelona water distribution network

12 páginas, 16 figuras, 1 tabla.-- El PDF es la versión pre-print.-- et al.This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results.This work is part of a applied research project granted by ADASA and AGBAR companies. The authors also wish to thank the support received by the Research Commission of the Generalitat of Catalunya (Group SAC Ref. 2009 SGR 1491) and by CICYT (Ref. HYFA DPI2008-01996 and WATMAN DPI2009-13744) of Spanish Ministry of Education.Peer reviewe

Validation and reconstruction of flow meter data in the Barcelona water distribution network

12 páginas, 16 figuras, 1 tabla.-- El PDF es la versión pre-print.-- et al.This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results.This work is part of a applied research project granted by ADASA and AGBAR companies. The authors also wish to thank the support received by the Research Commission of the Generalitat of Catalunya (Group SAC Ref. 2009 SGR 1491) and by CICYT (Ref. HYFA DPI2008-01996 and WATMAN DPI2009-13744) of Spanish Ministry of Education.Peer reviewe

Wavelet-based Burst Event Detection and Localization in Water Distribution Systems

In this paper we present techniques for detecting and locating transient pipe burst events in water distribution systems. The proposed method uses multiscale wavelet analysis of high rate pressure data recorded to detect transient events. Both wavelet coefficients and Lipschitz exponents provide additional information about the nature of the signal feature detected and can be used for feature classification. A local search method is proposed to estimate accurately the arrival time of the pressure transient associated with a pipe burst event. We also propose a graph-based localization algorithm which uses the arrival times of the pressure transient at different measurement points within the water distribution system to determine the actual location (or source) of the pipe burst. The detection and localization performance of these algorithms is validated through leak-off experiments performed on the WaterWiSe@SG wireless sensor network test bed, deployed on the drinking water distribution system in Singapore. Based on these experiments, the average localization error is 37.5 m. We also present a systematic analysis of the sources of localization error and show that even with significant errors in wave speed estimation and time synchronization the localization error is around 56 m.Singapore-MIT Alliance for Research and Technolog

Perancangan Letak Hidran Kebakaran pada Jaringan Distribusi Sistem Penyediaan Air Minum (Studi Kasus: Kecamatan Sayung, Kabupaten Demak)

Water supply in an effort to cope with a much needed fire. According Permen PU No.20 of 2009, the supply of water for fire fighting purposes is obtained from natural sources one river; or artificial like a fire hydrant. Supply of water from natural sources have problems on location erratic with the scene and sediment-borne debris or the charging process tank fire truck. While the fire hydrants, water supply derived from the distribution of clean water that could be a solution to these problems and the location of fire hydrants more regularly. SPAM distribution network planning Sayung includes 8 villages that are Urban Area Sayung conducted from year 2014 to 2029, with the need for water distribution at year-end planning of 273.37 l/s. Based on the city's strategic location and access roads, and fire risk classification (Permen PU No.20 of 2009), the location of fire hydrants in Sayung spread in the village Sriwulan, Loireng, Gemulak, Purwosari, and Sidogemah the number 12 fire hydrants. Results fire flow analysis using WaterCAD V8i show fire hydrants can be met discharge and pressure (31 l / s and 1.5 bar) for 24 hours, so it does not interfere with domestic water needs and non-domestic

Exploratory analysis of excitation-emission matrix fluorescence spectra with self-organizing maps as a basis for determination of organic matter removal efficiency at water treatment works

In the paper, the self-organizing map (SOM) was employed for the exploratory analysis of fluorescence excitation-emission data characterizing organic matter removal efficiency at 16 water treatment works in the UK. Fluorescence spectroscopy was used to assess organic matter removal efficiency between raw and partially treated (clarified) water to provide an indication of the potential for disinfection by-products formation. Fluorescence spectroscopy was utilized to evaluate quantitative and qualitative properties of organic matter removal. However, the substantial amount of fluorescence data generated impeded the interpretation process. Therefore a robust SOM technique was used to examine the fluorescence data and to reveal patterns in data distribution and correlations between organic matter properties and fluorescence variables. It was found that the SOM provided a good discrimination between water treatment sites on the base of spectral properties of organic matter. The distances between the units of the SOM map were indicative of the similarity of the fluorescence samples and thus demonstrated the relative changes in organic matter content between raw and clarified water. The higher efficiency of organic matter removal was demonstrated for the larger distances between raw and clarified samples on the map. It was also shown that organic matter removal was highly dependent on the raw water fluorescence properties, with higher efficiencies for higher emission wavelengths in visible and UV humic-like fluorescence centers

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLAS

Dynamics of Land Use and Land Cover Changes in Harare, Zimbabwe: A Case Study on the Linkage between Drivers and the Axis of Urban Expansion

With increasing population growth, the Harare Metropolitan Province has experienced accelerated land use and land cover (LULC) changes, influencing the city’s growth. This study aims to assess spatiotemporal urban LULC changes, the axis, and patterns of growth as well as drivers influencing urban growth over the past three decades in the Harare Metropolitan Province. The analysis was based on remotely sensed Landsat Thematic Mapper and Operational Land Imager data from 1984–2018, GIS application, and binary logistic regression. Supervised image classification using support vector machines was performed on Landsat 5 TM and Landsat 8 OLI data combined with the soil adjusted vegetation index, enhanced built-up and bareness index and modified difference water index. Statistical modelling was performed using binary logistic regression to identify the influence of the slope and the distance proximity characters as independent variables on urban growth. The overall mapping accuracy for all time periods was over 85%. Built-up areas extended from 279.5 km2 (1984) to 445 km2 (2018) with high-density residential areas growing dramatically from 51.2 km2 (1984) to 218.4 km2 (2018). The results suggest that urban growth was influenced mainly by the presence and density of road networks

Simulation of Water Distribution Systems

In this paper a software package offering a means of simulating complex water distribution systems is described. It has been developed in the course of our investigations into the applicability of neural networks and fuzzy systems for the implementation of decision support systems in operational control of industrial processes with case-studies taken from the water industry. Examples of how the simulation package have been used in a design and testing of the algorithms for state estimation, confidence limit analysis and fault detection are presented. Arguments for using a suitable graphical visualization techniques in solving problems like meter placement or leakage diagnosis are also given and supported by a set of examples