Managing expert knowledge in water network expansion project implementation

The implementation of expansion projects of water networks supplying growing cities is deemed to be a complex decision-making problem involving both technical aspects and expert knowledge. Management and control processes must rely on experts in the field whose knowhow must be coupled with techniques able to deal with the natural subjectivity that affects input evaluations. Given the presence of many decision-making elements, the choice of proper hydraulic technical parameters may be linked to the main aspects of analysis requiring formal expert evaluation. In this contribution, the simulation of hydraulic indicators is integrated with a multi-criteria approach able to eventually determine those areas of a water network through which organising the expansion may be more beneficial. The software EPAnet 2.0 is first used for hydraulic simulations, whereas the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) will eventually rank network's nodes. A case study is solved to demonstrate the applicability and effectiveness of the proposed approach. Copyright (C) 2021 The Authors

Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems

[EN] Prediction models have become essential for the improvement of decision-making processes in public management and, particularly, for water supply utilities. Accurate estimation often needs to solve multimeasurement, mixed-mode, and space-time problems, typical of many engineering applications. As a result, accurate estimation of real world variables is still one of the major problems in mathematical approximation. Several individual techniques have shown very good estimation abilities. However, none of them are free from drawbacks. This paper faces the challenge of creating accurate water demand predictive models at urban scale by using so-called committee machines, which are ensemble frameworks of single machine learning models. The proposal is able to combine models of varied nature. Specifically, this paper analyzes combinations of such techniques as multilayer perceptrons, support vector machines, extreme learning machines, random forests, adaptive neural fuzzy inference systems, and the group method for data handling. Analyses are checked on two water demand datasets from Franca (Brazil). As an ensemble tool, the combined response of a committee machine outperforms any single constituent model.Ambrosio, JK.; Brentan, BM.; Herrera Fernández, AM.; Luvizotto, E.; Ribeiro, L.; Izquierdo Sebastián, J. (2019). Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems. Mathematical Problems in Engineering. 2019:1-11. https://doi.org/10.1155/2019/97654681112019Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2010). Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems. Engineering Applications of Artificial Intelligence, 23(5), 727-735. doi:10.1016/j.engappai.2010.01.015Donkor, E. A., Mazzuchi, T. A., Soyer, R., & Alan Roberson, J. (2014). Urban Water Demand Forecasting: Review of Methods and Models. Journal of Water Resources Planning and Management, 140(2), 146-159. doi:10.1061/(asce)wr.1943-5452.0000314Adamowski, J. F. (2008). Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks. Journal of Water Resources Planning and Management, 134(2), 119-128. doi:10.1061/(asce)0733-9496(2008)134:2(119)Ghiassi, M., Zimbra, D. K., & Saidane, H. (2008). Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model. Journal of Water Resources Planning and Management, 134(2), 138-146. doi:10.1061/(asce)0733-9496(2008)134:2(138)Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559-583. doi:10.1016/0169-2070(89)90012-5Herrera, M., García-Díaz, J. C., Izquierdo, J., & Pérez-García, R. (2011). Municipal Water Demand Forecasting: Tools for Intervention Time Series. Stochastic Analysis and Applications, 29(6), 998-1007. doi:10.1080/07362994.2011.610161Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Barzegar, R., & Asghari Moghaddam, A. (2016). Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction. Modeling Earth Systems and Environment, 2(1). doi:10.1007/s40808-015-0072-8Nadiri, A. A., Gharekhani, M., Khatibi, R., Sadeghfam, S., & Moghaddam, A. A. (2017). Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Science of The Total Environment, 574, 691-706. doi:10.1016/j.scitotenv.2016.09.093Brentan, B. M., Meirelles, G., Herrera, M., Luvizotto, E., & Izquierdo, J. (2017). Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models. Mathematical Problems in Engineering, 2017, 1-10. doi:10.1155/2017/6343625Brentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532-541. doi:10.1016/j.cam.2016.02.009Johansson, C., Bergkvist, M., Geysen, D., Somer, O. D., Lavesson, N., & Vanhoudt, D. (2017). Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms. Energy Procedia, 116, 208-216. doi:10.1016/j.egypro.2017.05.068Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21-45. doi:10.1109/mcas.2006.1688199Ferreira, R. P., Martiniano, A., Ferreira, A., Ferreira, A., & Sassi, R. J. (2016). Study on Daily Demand Forecasting Orders using Artificial Neural Network. IEEE Latin America Transactions, 14(3), 1519-1525. doi:10.1109/tla.2016.7459644Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018Schölkop, B. (2003). An Introduction to Support Vector Machines. Recent Advances and Trends in Nonparametric Statistics, 3-17. doi:10.1016/b978-044451378-6/50001-6Huang, G.-B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics, 2(2), 107-122. doi:10.1007/s13042-011-0019-yIvakhnenko, A. G. (1970). Heuristic self-organization in problems of engineering cybernetics. Automatica, 6(2), 207-219. doi:10.1016/0005-1098(70)90092-

Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs) management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA) and machine learning powerful algorithms such as Self-Organizing Maps (SOMs) and Random Forest (RF). We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs) from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model

Graph Convolutional Recurrent Neural Networks for Water Demand Forecasting

Funder: Libera Università di Bolzano; Id: http://dx.doi.org/10.13039/501100008815Abstract: Short‐term forecasting of water demand is a crucial process for managing efficiently water supply systems. This paper proposes to develop a novel graph convolutional recurrent neural network (GCRNN) to predict time series of water demand related to some water supply systems or district metering areas that belong to the same geographical area. The aim is to build a graph‐based model able to capture the dependence among the different water demand time series both in spatial and in temporal terms. This model is built on a set of different graphs, and its performance is compared to two methods, including a state‐of‐the‐art deep long short‐term memory (LSTM) neural network and a traditional seasonal autoregressive moving average model. Additionally, the forecasting model is tested in a condition when a sensor has a malfunction. The results show the ability of the GCRNN to produce accurate and reliable forecasting, especially when based on graph built while accounting for both time‐series correlation and spatial criteria. The GCRNN consistently outperforms the LSTM during the fault test, showing its ability to generate a robust prediction for days after a sensor malfunction, given the GCRNN's ability to benefit from the other time series of the graph

Hybrid regression model for near real-time urban water demand forecasting

The most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of statistical, machine learning, and artificial intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement; and new challenges regarding on-line predictive models for water demand have appeared. This work proposes applying support vector regression, as one of the currently better machine learning options for short-term water demand forecasting, to build a base prediction. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes

Enhanced water demand analysis via symbolic approximation within an epidemiology-based forecasting framework

Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein developed. This combination represents a novel framework in urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic approximate (SAX) coding technique able to deal with simple versions of data sets are presented. Then, a neural-network-based model that uses SAX-based knowledge-generation from various time series is shown to improve forecasting abilities. This knowledge is produced by identifying water distribution district metered areas of high similarity to a given target area and sharing demand patterns with the latter. The proposal has been tested with databases from a Brazilian water utility, providing key knowledge for improving water management and hydraulic operation of the distribution system. This novel analysis framework shows several benefits in terms of accuracy and performance of neural network models for water demand

On-Line Cyber Attack Detection in Water Networks through State Forecasting and Control by Pattern Recognition

© ASCE. Water distribution systems are critical infrastructures that can be the subject of various types of attacks. Concerns range from biological and chemical intrusions in pipelines to operational issues in water control assets. Cyber-attacks have also turned today into a crucial issue to consider. In highly automatized infrastructures, cyber-attacks can be considered as non-planned actions changing system operation to non-expected scenarios. They can potentially produce unavailability of enough water appropriated for public consumption or critical services such as firefighting. This work proposes to tackle the identification of cyber-attack scenarios through an early-alarm system able to recognize patterns corresponding to abnormal working conditions of the system. Firstly, an on-line forecasting model is developed that is based on the water system expected state regarding nodal pressures, tank water levels and control device flows. Then, an approach based on non-linear autoregressive networks with exogenous inputs (NARX) is proposed to take advantage of both their computational efficiency and the strong influence of the periodicity of the inputs under study. Finally, an analysis of abrupt change point, conducted in a time series composed by the differences between the observed measurements and the expected data, is built on top of the forecasting model