Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Water Resources Management
Published in: Water Resources Management 3/2020

28-01-2020

A Method for Predicting Long-Term Municipal Water Demands Under Climate Change

Authors: Salah L. Zubaidi, Sandra Ortega-Martorell, Patryk Kot, Rafid M. Alkhaddar, Mawada Abdellatif, Sadik K. Gharghan, Maytham S. Ahmed, Khalid Hashim

Published in: Water Resources Management | Issue 3/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The accurate forecast of water demand is challenging for water utilities, specifically when considering the implications of climate change. As such, this is the first study that focuses on finding associations between monthly climate factors and municipal water consumption, using baseline data collected between 1980 and 2010. The aim of the study was to investigate the reliability and capability of a combination of techniques, including Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANNs), to accurately predict long-term, monthly water demands. The principal findings of this research are as follows: a) SSA is a powerful method when applied to remove the impact of socio-economic variables and noise, and to determine a stochastic signal for long-term water consumption time series; b) ANN performed better when optimised using the Lightning Search Algorithm (LSA-ANN) compared with other approaches used in previous studies, i.e. hybrid Particle Swarm Optimisation (PSO-ANN) and Gravitational Search Algorithm (GSA-ANN); c) the proposed LSA-ANN methodology was able to produce a highly accurate and robust model of water demand, achieving a correlation coefficient of 0.96 between observed and predicted water demand when using a validation dataset, and a very small root mean square error of 0.025.
previous article Generation of Clean Hydropower Energy in Multi-Reservoir Systems Based on a New Evolutionary Algorithm
next article Acknowledgement of Reviewers 2019

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
Abrahart RJ, Kneale PE, See LM (2004) Neural Networks for Hydrological Modelling. Taylor & Francis Group plc, London
Adamowski JF (2008) Peak daily water demand forecast modeling using artificial neural networks. J Water Resour Plan Manag 134:119–128
Ahmed M, Mohamed A, Homod R, Shareef H (2016) Hybrid LSA-ANN based home energy management scheduling controller for residential demand response strategy. Energies 9:716
Al-Bugharbee H, Trendafilova I (2016) A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling. J Sound Vib 369:246–265
Altunkaynak A, Nigussie TA (2018) Monthly water demand prediction using wavelet transform, first-order differencing and linear detrending techniques based on multilayer perceptron models. Urban Water J 15:177–181
Anele A, Hamam Y, Abu-Mahfouz A, Todini E (2017) Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction. Water, 9, 877:1–12
Data driven modelling: using Matlab in water resources and environmental engineering. In: Singh, VP (ed.) Water science and technology library. New York: Springer, pp. 103–109
Behboudian S, Tabesh M, Falahnezhad M, Ghavanini FA (2014) A long-term prediction of domestic water demand using preprocessing in artificial neural network. J Water Supply Res Technol AQUA 63:31–42
Bhavani R (2013) Comparision of mean and weighted annual rainfall in Anantapuram District. Int J Innov Res Sci Eng Technol 2:7
Bougadis J, Adamowski K, Diduch R (2005) Short-term municipal water demand forecasting. Hydrol Process 19:137–148
Brentan BM, Meirelles G, Herrera M, Luvizotto E, Izquierdo J (2017) Correlation analysis of water demand and predictive variables for short-term forecasting models. Math Probl Eng 2017:1–10
Cutore P, Campisano A, Kapelan Z, Modica C, Savic D (2008) Probabilistic prediction of urban water consumption using the SCEM-UA algorithm. Urban Water J 5:125–132
Donkor EA, Mazzuchi TH, Soyer R, Roberson JA (2014) Urban water demand forecasting: review of methods and models. J Water Resour Plan Manag 140:146–159
Feng Z-K, Niu W-J, Zhang R, Wang S, Cheng C-T (2019) Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization. J Hydrol 576:229–238
Fenta Mekonnen D, Disse M (2018) Analyzing the future climate change of upper Blue Nile River basin using statistical downscaling techniques. Hydrol Earth Syst Sci 22:2391–2408
Ferguson BC, Brown RR, Frantzeskaki N, DE Haan FJ, Deletic A (2013) The enabling institutional context for integrated water management: lessons from Melbourne. Water Res 47:7300–7314
García-Ródenas R, Linares LJ, López-Gómez JA (2019) A Memetic chaotic gravitational search algorithm for unconstrained global optimization problems. Appl Soft Comput 79:14–29
Gato S, Jayasuriya N, Hadgraft R (2005) A simple time series approach to modelling urban water demand. Aust J Water Resour 8:153–164
Gato S, Jayasuriya N, Roberts P (2007) Temperature and rainfall thresholds for base use urban water demand modelling. J Hydrol 337:364–376
Gharghan SK, Nordin R, Ismail M, Ali JA (2016) Accurate wireless sensor localization technique based on hybrid pso-ann algorithm for indoor and outdoor track cycling. Inst Electr Electron Eng Sensors J 16:529–541
Ghodsi M, Hassani H, Sanei S, Hicks Y (2009) The use of noise information for detection of temporomandibular disorder. Biomed Signal Process Control 4:79–85
Golyandina N, and Zhigljavsky A (2013) Singular Spectrum analysis for time series, USA, Springer
Guo G, Liu S, Wu Y, Li J, Zhou R, Zhu X (2018) Short-Term Water Demand Forecast Based on Deep Learning Method. J Water Resour Plan Manag 2, 144:1–11
Hossain I, Esha R, Alam Imteaz M (2018). An attempt to use non-linear regression Modelling technique in long-term seasonal rainfall forecasting for Australian Capital Territory. Geosciences 8
Kadiyala MD, Nedumaran S, Singh P, S C, Irshad MA, Bantilan MC (2015) An integrated crop model and GIS decision support system for assisting agronomic decision making under climate change. Sci Total Environ 521-522:123–134
Karami H, Farzin S, Jahangiri A, Ehteram M, Kisi O, El-Shafie A (2019) Multi-reservoir system optimization based on hybrid gravitational algorithm to minimize water-supply deficiencies. Water Resour Manag 33:2741–2760
Khan MAR, Poskitt DS (2017) Forecasting stochastic processes using singular spectrum analysis: aspects of the theory and application. Int J Forecast 33:199–213
Lai DTC, Miyakawa M, Sato Y (2019) Semi-supervised data clustering using particle swarm optimisation. Soft Comput
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124
Marlow DR, Moglia M, Cook S, Beale DJ (2013) Towards sustainable urban water management: a critical reassessment. Water Res 47:7150–7161
Meshram SG, Ghorbani MA, Deo RC, Kashani MH, Meshram C, Karimi V (2019) New approach for sediment yield forecasting with a two-phase feedforward neuron network-particle swarm optimization model integrated with the gravitational search algorithm. Water Resour Manag 33:2335–2356
Mouatadid S, Adamowski J (2016) Using extreme learning machines for short-term urban water demand forecasting. Urban Water J 14:630–638
Mutlag A, Mohamed A, Shareef H (2016) A nature-inspired optimization-based optimum fuzzy logic photovoltaic inverter controller utilizing an eZdsp F28335 board. Energies 99, 120:1–32
Mutlu E, Chaubey I, Hexmoor H, Bajwa SG (2008) Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol Process 22:5097–5106
Osman YZ, Abdellatif M, Al-ansari N, Knutsson S, Jawad S (2017) Climate change and future precipitation in an arid environment of the MIDDLE EAST: CASE study of Iraq. J Environ Hydrol 25:1–18
Ouyang Q, Lu W (2017) Monthly rainfall forecasting using Echo state networks coupled with data preprocessing methods. Water Resour Manag 32:659–674
Özkaraca O, Keçebaş A (2019) Performance analysis and optimization for maximum exergy efficiency of a geothermal power plant using gravitational search algorithm. Energy Convers Manag 185:155–168
Pacchin E, Gagliardi F, Alvisi S, Franchini M (2019) A comparison of short-term water demand forecasting models. Water Resour Manag 33:1481–1497
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: A gravitational search algorithm. Inf Sci 179:2232–2248
Sebri M (2013) ANN versus SARIMA models in forecasting residential water consumption in Tunisia. J Water Sanit Hyg Dev 3:330–340
Seo Y, Kwon S, Choi Y (2018) Short-term water demand forecasting model combining Variational mode decomposition and extreme learning machine. Hydrology 5
Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput 36:315–333
Tabachnick BG, and Fidell LS (2013) Using multivariate statistics, United States of America, Pearson Education, Inc
Tian D, Martinez CJ, Asefa T (2016) Improving short-term urban water demand forecasts with reforecast analog ensembles. J Water Resour Plan Manag 142
Toth E, Bragalli C, Neri M (2018) Assessing the significance of tourism and climate on residential water demand: panel-data analysis and non-linear modelling of monthly water consumptions. Environ Model Softw 103:52–61
Urich C, Rauch W (2014) Exploring critical pathways for urban water management to identify robust strategies under deep uncertainties. Water Res 66:374–389
Zhang X, Chen N, Sheng H, Ip C, Yang L, Chen Y, Sang Z, Tadesse T, Lim TPY, Rajabifard A, Bueti C, Zeng L, Wardlow B, Wang S, Tang S, Xiong Z, Li D, Niyogi D (2019) Urban drought challenge to 2030 sustainable development goals. Sci Total Environ 693:133536
Zhoua SL, Mcmahon TA, Walton A, Lewis J (2000) Forecasting daily urban water demand: a case study of Melbourne. J Hydrol 236:153–164
Zubaidi SL, Kot P, Alkhaddar RM, Abdellatif M, Al-Bugharbee H (2018) Short-Term Water Demand Prediction in Residential Complexes: Case Study in Columbia City, USA. 2018 11th International Conference on Developments in eSystems Engineering (DeSE), 2–5 Sept. 2018c Cambridge, United Kingdom. IEEE, 31–35
Zubaidi SL, Dooley J, Alkhaddar RM, Abdellatif M, Al-bugharbee H, Ortega-Martorell S (2018a) A novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks. J Hydrol 561:136–145
Zubaidi SL, Gharghan SK, Dooley J, Alkhaddar RM, Abdellatif M (2018b) Short-term urban water demand prediction considering weather factors. Water Resour Manag
Metadata
Title
A Method for Predicting Long-Term Municipal Water Demands Under Climate Change
Authors
Salah L. Zubaidi
Sandra Ortega-Martorell
Patryk Kot
Rafid M. Alkhaddar
Mawada Abdellatif
Sadik K. Gharghan
Maytham S. Ahmed
Khalid Hashim
Publication date
28-01-2020
Publisher
Springer Netherlands
Published in
Water Resources Management / Issue 3/2020
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-020-02500-z

Other articles of this Issue 3/2020

Water Resources Management 3/2020 Go to the issue

OriginalPaper

Cascaded Hydropower Operation Chart Optimization Balancing Overall Ecological Benefits and Ecological Conservation in Hydrological Extremes Under Climate Change

OriginalPaper

Enhanced Channel Division Method for Estimation of Discharge in Meandering Compound Channel

OriginalPaper

Quantity of Reasonable Distribution of River Ecological Basic Flow Considering the Economic Value of its Own Ecological Functions: a Case Study in the Baoji Section of the Weihe River, China

OriginalPaper

Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms

OriginalPaper

Ensemble Based Forecasting and Optimization Framework to Optimize Releases from Water Supply Reservoirs for Flood Control

EditorialNotes

Acknowledgement of Reviewers 2019