DAILY MULTIVARIATE FORECASTING OF WATER DEMAND IN A TOURISTIC ISLAND WITH THE USE OF ANN AND ANFIS

Transcription

1 DAILY MULTIVARIATE FORECASTING OF WATER DEMAND IN A TOURISTIC ISLAND WITH THE USE OF ANN AND ANFIS D. Kofinas, E. Papageorgiou, C. Laspidou, N. Mellios, K. Kokkinos

2 CONTEMPORARY APPROACHES ON WDS MANAGEMENT dealing with temporal and spatial pressure variation within the networks dividing the network in DMAs employing a DSS ICT involvement matching water demand with water supply optimization of pressure in the network real-time monitoring water demand forecasting energy savings leakage minimization reduction of pipe bursts, green-house gas emission chemical use overall reduction of Non-Revenue Water outcomes

3 PREDICTING WATER DEMAND models linear and non-linear univariate and multivariate evolutionary water demand multi-variate investigation is superior to univariate analysis sheds light on drivers forms the basis of a managerial tool

4 MEDITERRANEAN TOURISTIC RESORTS massive leakages aging infrastructure poorly designed water networks bold relief elevated households absence of DMAs high pressure intense seasonality of water demand arid and hot summers with large touristic influxes wet winters with low water consumption shortage of water resources Groundwater in diminishing quantity water demand forecasting: basic step

5 IN THIS WORK integrated infrastructure consists of downstream and upstream data collection components using sensor technologies connecting to a DSS platform. as a function of mean temperature, high temperature, precipitation, touristic influx, network leakage the two forecasting methods are tested for numerous possible architectures and their adequacy is evaluated on an independent testing period data set through appropriate metrics. daily water demand in a Mediterranean touristicisland Adaptive Neuro-Fuzzy Inference System, ANFIS and Artificial Neural Network, ANN are applied

6 STUDY SITE AND DATA Skiathos WDS groundwater a small town of 3,500 water consumers not divided into DMAs no pressure regulation aged network leakage that rises up to even 70% of water demand weather variables drive water demand in a rather nonlinear way. linear effect of precipitation questioned: response of water consumers is psychological and concerns the incident of rain more than the amount typical Mediterranean climate meteorological variables are correlated to Skiathos water demand mean and high temperature: significant positive correlation precipitation: negative multicollinearity of meteorological and touristic variables

7 water demand follows touristic inflow touristic season: Apr Sep sharp peak in Aug Summer demand up to more than 170% of winter demand leakage seems to increase with time highest leakage recorded in winter months, when network pressure is high due to reduced demand typical water uses: household use variety of touristic-related businesses small hotels, taverns, restaurants, pubs and cafes big hotels have their own drillings

8 5 PREDICTORS ANN and ANFIS: applied for numerous possible architectures, in order to find the most fitting ANN and ANFIS structure data sets: Jan 2011 to Jul 2015 divided to 7:3, training:testing sets 3.5 years for training, one year for testing

9 ARTIFICIAL NEURAL NETWORK system of interconnected units known as neurons interacting across weighted connections inspired by the architecture of the human brain ANN can be classified into supervised and unsupervised learning methods neurons learn complex patterns of information generalize the learned information feedforward and feedback recall architectures compute output values from inputs

10 SUCCESS OF ANN IN PREDICTION alternative to time series forecasting major advantage of NNs: flexible nonlinear modeling no need to specify a particular model form values of network parameters selected after experiments considering all different cases of: the model is adaptively formed based on the data suitable for empirical data sets where no theoretical guidance is available values of momentum and learning parameters number of hidden layers learning rate and momentum parameters, two learning algorithms of backpropagation ANN technique Levenberg-Marquardt (LM) and gradient decent (GD) number of epochs configurations that produce best results from conducted experiments were selected

11 ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM provides an intuition for relationship of input and output sets uses fuzzy logic principles establishes input-output relationship through a rule based inference engine consists of: a rule-base, containing fuzzy if then rules a data-base, defining the membership functions an inference system, combining the fuzzy rules and producing the system results

12 two types of popular FIS: Takagi Sugeno and Mamdani they differ in the definition of the consequent parameters in the network in this study: Takagi-Sugeno in which rule base constructed from input-output pairs and consists of 5 layers input fuzzification Fuzzy set database construction Fuzzy rule base construction decision making output defuzzification

13 METRICS assessed to evaluate effectiveness of two methods and select the best fitting architecture: root-mean-square error (RMSE) mean absolute error (MAE) mean percentage error (MPE) mean absolute percentage error (MAPE) Nash Sutcliffe model efficiency coefficient (E) used for predictive power of hydrological models R 2

14 REGARDING ANFIS Matlab: ANFIS-Editor utilize different variables number of membership function, MF type of MF type of output MF optimization method (hybrid or back propagation) number of epochs triangular (trimf) generalized bell-shaped (gbelmf) Gaussian (gaussmf) Gaussian combination (gauss2mf) trapezoidal (trapmf) Π-shaped (pimf) sigmoidal (dsigmf)] All possible combinations of MF numbers for each predictor from 2 to 4 and MF types implemented

15 RESULTS AND DISCUSSION best ANN one hidden layer with 10 neurons learning rate = 0.3 momentum = 0.1 best ANFIS Number of MFs: x 1 :3, x 2 :2, x 3 :2, x 4 :2, x 5 :3 Fuzzification: triangular shaped function for all variables Partitioning: grid partitioning method daily forecast of water demand seems to be achievable at satisfactory accuracy as tested for numerous ANN and ANFIS structures

16 COMPARISON OF METHODS both methods adequate ANFIS gives better results in all metrics of accuracy weakest yet acceptable fitting Oct Dec 2014 a result of using single leakage rate throughout trimester acceptable misfοrecast: hydrological models outputs are only put into practice by operators, after applying safety factors metrics and overall predictions will significantly improve if more accurate leakage rate data are included

17 CONCLUSIONS an approach for multivariate daily prediction of water demand in a highly touristic Mediterranean resort two forecasting methods: ANN and ANFIS drivers of water demand: mean and high temperature precipitation inflow of tourists leakage level two quite satisfying models: overcome any non-linearity or collinearity issues ANFIS method gives better results useful forecasting tool for water management

18 This work was supported by the project ISS EWATUS Integrated Support System for Efficient Water Usage and Resources Management which is implemented in the framework of the EU 7th Framework Programme, Specific programme Cooperation Information and Communication Technologies; Grant Agreement Number