Short-term water demand forecasting using artificial neural networks: IIT Kanpur experience

Jain, Deepak; Joshi, U.; Varshney, Ashish Kumar

doi:10.1109/icpr.2000.906111

Cited by 76 publications

(126 citation statements)

References 2 publications

(2 reference statements)

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“…Sustainability 2017, 9, 1193 12 of 15 ANN (30) showed the highest accuracy among all results, and ANN (20) of the original data represented the least biased NRW ratio. Figure 7 shows the results of a scatter plot analysis of original data without using the Z-score method.…”

Section: Analysis Of Estimation Results Of Nrw Ratio Via Annmentioning

confidence: 99%

“…The ANN (30) with 30 neurons showed the highest accuracy among all assessment regulations of the PBIAS, MAE, MSE and G-value when the outlier was removed by the Z-score method. As a result of analyzing the data with the outlier removed by the Z-score, the accuracy of ANN (20) and ANN (30) was found higher than that of the original data used as a condition. ANN (30) showed the highest accuracy among all results, and ANN (20) of the original data represented the least biased NRW ratio.…”

Section: Analysis Of Estimation Results Of Nrw Ratio Via Annmentioning

confidence: 99%

“…As a result of analyzing the data with the outlier removed by the Z-score, the accuracy of ANN (20) and ANN (30) was found higher than that of the original data used as a condition. ANN (30) showed the highest accuracy among all results, and ANN (20) of the original data represented the least biased NRW ratio. Figure 7 shows the results of a scatter plot analysis of original data without using the Z-score method.…”

Section: Analysis Of Estimation Results Of Nrw Ratio Via Annmentioning

confidence: 99%

“…A procedure to devise a general operating policy toward reservoir operation from a dynamic programming using neural network (DPN) was suggested [19]. Relatively new technique of using ANNs researched for forecasting short-term water demand [20]. ANNs in water quality modeling, as well as for the process and control of treating drinking water used in water distribution systems [21].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Non-Revenue Water Ratio for Sustainable Management Using Artificial Neural Network and Z-Score in Incheon, Republic of Korea

Jang

Choi

2017

Sustainability

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Abstract:The non-revenue water (NRW) ratio in a water distribution system is the ratio of the loss due to unbilled authorized consumption, apparent losses and real losses to the overall system input volume (SIV). The method of estimating the NRW ratio by measurement might not work in an area with no district metered areas (DMAs) or with unclear administrative district. Through multiple regression analyses is a statistical analysis method for calculating the NRW ratio using the main parameters of the water distribution system, although its disadvantage is lower accuracy than that of the measured NRW ratio. In this study, an artificial neural network (ANN) was used to estimate the NRW ratio. The results of the study proved that the accuracy of NRW ratio calculated by the ANN model was higher than by multiple regression analysis. The developed ANN model was shown to have an accuracy that varies depending on the number of neurons in the hidden layer. Therefore, when using the ANN model, the optimal number of neurons must be determined. In addition, the accuracy of the outlier removal condition was higher than that of the original data used condition.

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Section: Analysis Of Estimation Results Of Nrw Ratio Via Annmentioning

confidence: 99%

Section: Analysis Of Estimation Results Of Nrw Ratio Via Annmentioning

confidence: 99%

Section: Analysis Of Estimation Results Of Nrw Ratio Via Annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Non-Revenue Water Ratio for Sustainable Management Using Artificial Neural Network and Z-Score in Incheon, Republic of Korea

Jang

Choi

2017

Sustainability

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“…Stratified sampling was applied to ensure that the statistics of the testing and training data matched closely. In addition, different performance indicators, namely the mean square error (MSE), sum squared error (SSE), average absolute relative error (AARE), Nash-Sutcliffe efficiency coefficient (E), and Pearson product moment coefficient of correlation (R) (Hanspal et al 2013;Jain et al 2001) were utilized to assess the robustness and accuracy of the ANN models with different neuron numbers. Performance parameters were computed for the entire dataset (i.e.…”

Section: Modelling Approachmentioning

confidence: 99%

Artificial Neural Network to Determine Dynamic Effect in Capillary Pressure Relationship for Two-Phase Flow in Porous Media with Micro-Heterogeneities

et al. 2014

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An artificial neural network (ANN) is presented for computing a parameter of dynamic two-phase flow in porous media with water as wetting phase, namely, dynamic coefficient (τ), by considering micro-heterogeneity in porous media as a key parameter. τ quantifies the dependence of time derivative of water saturation on the capillary pressures and indicates the rates at which a two-phase flow system may reach flow equilibrium. Therefore, τ is of importance in the study of dynamic two-phase flow in porous media. An attempt has been made in this work to reduce computational and experimental effort by developing and applying an ANN which can predict the dynamic coefficient through the "learning" from available data. The data employed for testing and training the ANN have been obtained from computational flow physics-based studies. Six input parameters have been used for the training, performance testing and validation of the ANN which include water saturation, intensity of heterogeneity, average permeability depending on this intensity, fluid density ratio, fluid viscosity ratio and temperature. It is found that a 15 neuron, single hidden layer ANN can characterize the relationship between media heterogeneity and dynamic coefficient and it ensures a reliable prediction of the dynamic coefficient as a function of water saturation.

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Water consumption patterns as a basis for water demand modeling

Avni

Fishbain

Shamir

2015

Water Resources Research

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Future water demand is a main consideration in water system management. Consequently, water demand models (WDMs) have evolved in past decades, identifying principal demand‐generating factors and modeling their influence on water demand. Regional water systems serve consumers of various types (e.g., municipalities, farmers, industrial regions) and consumption patterns. Thus, one of the challenges in regional water demand modeling is the heterogeneity of the consumers served by the water system. When a high‐resolution, regional WDM is desired, accounting for this heterogeneity becomes all the more important. This paper presents a novel approach to regional water demand modeling. The two‐step approach includes aggregating the data set into groups of consumers having similar consumption characteristics, and developing a WDM for each homogeneous group. The development of WDMs is widely applied in the literature and thus, the focus of this paper is to discuss the first step of data aggregation. The research hypothesis is that water consumption records in their original or transformed form can provide a basis for aggregating the data set into groups of consumers with similar consumption characteristics. This paper presents a methodology for water consumption data clustering by comparing several data representation methods (termed Feature Vectors): monthly normalized average, monthly consumption coefficient of variation, a combination of the monthly average and monthly variation, and the autocorrelation coefficients of the consumption time series. Clustering using solely normalized monthly average provided homogeneous and distinct clusters with respect to monthly consumption, which succeed in capturing different consumer characteristics (water use, geographical location) that were not specified a‐priori. Clustering using the monthly coefficient of variation provided different, yet homogeneous clusters, clustering consumers characterized by similar variation trends that were closely related to consumer water use type. The concatenation of these two Feature Vectors provided further insight into the relationship between consumption patterns and variability of consumers. An autocorrelation Feature Vector provided results that can form a basis for constructing a time‐series model that is based on a group of resembling time series. The approaches presented here are steps toward utilizing the increasing amount of available water consumption data and data analysis techniques to facilitate the modeling of water demands in larger and heterogeneous regions with sufficient resolution.

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Short-term water demand forecasting using artificial neural networks: IIT Kanpur experience

Cited by 76 publications

References 2 publications

Estimation of Non-Revenue Water Ratio for Sustainable Management Using Artificial Neural Network and Z-Score in Incheon, Republic of Korea

Estimation of Non-Revenue Water Ratio for Sustainable Management Using Artificial Neural Network and Z-Score in Incheon, Republic of Korea

Artificial Neural Network to Determine Dynamic Effect in Capillary Pressure Relationship for Two-Phase Flow in Porous Media with Micro-Heterogeneities

Water consumption patterns as a basis for water demand modeling

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