scholarly journals Differential Learning for Outliers: A Case Study of Water Demand Prediction

2018 ◽  
Vol 8 (11) ◽  
pp. 2018 ◽  
Author(s):  
Setu Shah ◽  
Zina Ben Miled ◽  
Rebecca Schaefer ◽  
Steve Berube
Keyword(s):  
Water Demand ◽  
Water Distribution ◽  
Network Models ◽  
Recurrent Network ◽  
Machine Learning Techniques ◽  
Water Utility ◽  
Differential Model ◽  
Differential Learning ◽  
Water Demands ◽  
Utility Companies

Predicting water demands is becoming increasingly critical because of the scarcity of this natural resource. In fact, the subject was the focus of numerous studies by a large number of researchers around the world. Several models have been proposed that are able to predict water demands using both statistical and machine learning techniques. These models have successfully identified features that can impact water demand trends for rural and metropolitan areas. However, while the above models, including recurrent network models proposed by the authors are able to predict normal water demands, most have difficulty estimating potential deviations from the norms. Outliers in water demand can be due to various reasons including high temperatures and voluntary or mandatory consumption restrictions by the water utility companies. Estimating these deviations is necessary, especially for water utility companies with a small service footprint, in order to efficiently plan water distribution. This paper proposes a differential learning model that can help model both over-consumption and under-consumption. The proposed differential model builds on a previously proposed recurrent neural network model that was successfully used to predict water demand in central Indiana.

scholarly journals Pipe replacement by age only, how misleading could it be?

2018 ◽  
Vol 19 (3) ◽  
pp. 846-854 ◽  
Author(s):  
M. A. Pardo ◽  
J. Valdes-Abellan
Keyword(s):  
Water Distribution ◽  
Energy Savings ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Water Utility ◽  
Efficient Management ◽  
Utility Companies ◽  
Pipe Replacement ◽  
Energy Audits ◽  
The Impact

Abstract Traditional methods for prioritizing the renewal of water are based on heuristic models, such as the number of breaks per length, rule-of-thumb, and records held by the water utility companies. Efficient management of water distribution networks involves factoring in water and energy losses as the key criteria for planning pipe renewal. Prioritizing the replacement of a pipe according to the highest value of unit headloss due to ageing does not consider the impact on water and energy consumption for the whole network. Thus, this paper proposes a methodology to prioritize pipe replacement according to water and energy savings per monetary unit invested – economic prioritization. This renewal plan shows different results if comparing with replacing pipelines with regard to age and it requires calculating water and energy audits of the water distribution networks. Moreover, the required time to recover the investment performed needs to be calculated. The methodology proposed in this work is compared with the unit headloss criterion used in a real water-pressurized network. The results demonstrate that using the unit headloss criterion neither water, energy nor the investment is optimized. Significant water and energy savings are not fully exploited.

scholarly journals Review of model-based and data-driven approaches for leak detection and location in water distribution systems

2021 ◽  
Author(s):  
Zukang Hu ◽  
Beiqing Chen ◽  
Wenlong Chen ◽  
Debao Tan ◽  
Dingtao Shen
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Leak Detection ◽  
Data Driven ◽  
Detection Methods ◽  
Water Utility ◽  
Model Based ◽  
Utility Companies ◽  
Leak Detection And Location

Abstract Leak detection and location in water distribution systems (WDSs) is of utmost importance for reducing water loss, which is, however, a major challenge for water utility companies. To this end, researchers have proposed a multitude of methods to detect such leaks in WDSs. Model-based and data-driven approaches, in particular, have found widespread uses in this area. In this paper, we reviewed both these approaches and classified the techniques used by them according to their leak detection methods. It is seen that model-based approaches require highly calibrated hydraulic models, and their accuracies are sensitive to modeling and measurement uncertainties. On the contrary, data-driven approaches do not require an in-depth understanding of the WDS. However, they tend to result in high false positive rates. Furthermore, neither of these approaches can handle anomalous variations caused by unexpected water demands.

WaterNetGen: an EPANET extension for automatic water distribution network models generation and pipe sizing

2012 ◽  
Vol 12 (1) ◽  
pp. 117-123 ◽  
Author(s):  
João Muranho ◽  
Ana Ferreira ◽  
Joaquim Sousa ◽  
Abel Gomes ◽  
Alfeu Sá Marques
Keyword(s):  
Water Demand ◽  
Water Distribution ◽  
Maximum Velocity ◽  
Network Models ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Minimum Diameter ◽  
Synthetic Models ◽  
Total Water Demand ◽  
Selection Of

This paper focuses on the generation of synthetic models of water distribution networks (WDN). Models are widely used in many fields related with WDN planning and operation. Therefore, the main contribution of this work is to provide an automatic procedure to build models with the well-known EPANET tool in a manner that, with a small amount of input data and a few clicks, the user can build a network topology and assign suitable pipe diameters. For that purpose, a new application, called WaterNetGen, was designed and implemented as an extension to the EPANET software. WaterNetGen can be used to generate synthetic models of WDN, with several hundred nodes and pipes, within a few minutes. The sizing capability allows the selection of commercial diameters, such that the final network design satisfies certain user-defined design constraints, like minimum diameter, maximum velocity and minimum pressure. The total water demand is allocated to the pipes taking into account their length and a demand coefficient. The water demand of each pipe is then assigned to its start and end nodes and follows a specific demand pattern.

scholarly journals Disinfection by-products in desalinated and blend water: formation and control strategy

2018 ◽  
Vol 17 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Shakhawat Chowdhury
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Cancer Risks ◽  
Domestic Water ◽  
Desalinated Water ◽  
Water Demands ◽  
Product Water ◽  
And Control ◽  
Different Sources ◽  
By Products

Abstract Desalinated seawater is the major source of drinking water in many countries. During desalination, several activities including pretreatment, desalination, stabilization, mixing, storage and distribution are performed. Few disinfectants are used during these activities to control the biofouling agents and microbiological regrowth. The reactions between the disinfectants and natural organic matter (NOM), bromide and iodide form disinfection by-products (DBPs) in product water. The product water is stabilized and mixed with treated freshwater (e.g., groundwater) to meet the domestic water demands. The DBPs in desalinated and blend water are an issue due to their possible cancer and non-cancer risks to humans. In this paper, formation and distribution of DBPs in different steps of desalination and water distribution systems prior to reaching the consumer tap were reviewed. The variability of DBPs among different sources and desalination processes was explained. The toxicities of DBPs were compared and the strategies to control DBPs in desalinated water were proposed. Several research directions were identified to achieve comprehensive control on DBPs in desalinated water, which are likely to protect humans from the adverse consequences of DBPs.

scholarly journals Simulating a Stochastic Signal of Urban Water Demand by a Novel Combination of Data Analytic and Machine Learning Techniques

2021 ◽  
Vol 1058 (1) ◽  
pp. 012066
Author(s):  
Salah L. Zubaidi ◽  
Hussein Al-Bugharbee ◽  
Yousif Raad Muhsin ◽  
Sadik Kamel Gharghan ◽  
Khalid Hashim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Machine Learning Techniques ◽  
Urban Water ◽  
Stochastic Signal ◽  
Urban Water Demand ◽  
Learning Techniques ◽  
Data Analytic

scholarly journals Recurrent Network Models of Sequence Generation and Memory

Neuron ◽  
2016 ◽  
Vol 90 (1) ◽  
pp. 128-142 ◽  
Author(s):  
Kanaka Rajan ◽  
Christopher D. Harvey ◽  
David W. Tank
Keyword(s):  
Network Models ◽  
Recurrent Network ◽  
Sequence Generation

Future Climatic Patterns and Sustainability of Current Cropping Patterns in a Water-Scarce River Basin of Eastern India 

2021 ◽  
Author(s):  
Smaranika Mahapatra ◽  
Madan Kumar Jha
Keyword(s):  
Water Demand ◽  
Agricultural Sector ◽  
Monsoon Rainfall ◽  
Baseline Period ◽  
Eastern India ◽  
Crop Water ◽  
Cropping Patterns ◽  
Water Demands ◽  
Crop Water Demand ◽  
Water Scarce

<p>Agricultural sector, being the largest consumer of water is greatly affected by climatic variability and disasters. Most parts of the world already face an enormous challenge in meeting competitive and conflicting multi-sector water demands. Climate change has further exacerbated this challenge by putting the sustainability of current cropping patterns and irrigation practices in question. For ensuring climate-resilient food production, it is crucial to examine the patterns of the projected climate and potential impacts on the agricultural sector at a basin scale. Hence, this study was carried out for an already water-scarce basin, Rushikulya River basin (RRB), located in the coastal region of eastern India. The bias-corrected NorESM2-MM general circulation model of Coupled Model Intercomparison Project-6 (CMIP6) was used in this study under four shared socioeconomic pathway (SSPs) scenarios, namely SSP126, SSP245, SSP370 and SSP585. The projected climatic parameters and crop water demands of the basin were analyzed assuming existing cropping pattern in the future. Analysis of the results reveals a significant and rapid increase in the temperature at a rate of 0.02-0.5ºC/year during 2026-2100 under all SSPs except SSP126, whereas the rainfall is expected to increase slightly during 2026-2100 as compared to the baseline period (1990-2016), especially in the far future (2076-2100) under all the SSPs. In contrast, monsoon rainfall is predicted to decrease under SSP245 and SSP370, while a slight increase in the monsoon rainfall is evident under SSP126 and SSP585. Although the rainy days will decrease slightly in the future 25-year time window, the number of heavy rainfall events is predicted to increase by two to three times. Also, retrospective analysis of rainfall and evapotranspiration suggested an existence of rainfall deficit (rainfall-evapotranspiration) in the basin throughout the year, except during July to September. The rainfall deficit in the basin during 2026-2100 is found to remain more or less same in the non-monsoon season, except for the month of October under SSP245, SSP370 and SSP585 scenarios where deficit increases by two folds. Rainfall is expected to be in surplus by 4 to 5 times higher under all SSPs except for SSP245. As to the evapotranspiration, an insignificant increasing trend is observed under future climatic condition with only 2 to 4% rise in the crop water demand compared to the baseline period. As the basin is already water stressed during most months in a year under baseline and future climatic conditions, continuing the current practice of monsoon paddy dominant cultivation in the basin will further aggravate this situation. The results of this study will be helpful in formulating sustainable irrigation plans and adaptation measures to address climate-induced water stress in the basin.</p><p><strong>Keywords:</strong> Climate change; CMIP6; SSP; Monsoon rainfall; Temperature; Crop water demand.</p>

Energy Benchmarking of Water and Wastewater Treatment, Distribution and Collection: A Case Study of Austin Water Utility

2013 ◽  
Author(s):  
Jill B. Kjellsson ◽  
David Greene ◽  
Raj Bhattarai ◽  
Michael E. Webber
Keyword(s):  
Water Distribution ◽  
Energy Intensity ◽  
Potable Water ◽  
Energy Savings ◽  
Operating Cost ◽  
Electric Energy ◽  
Water And Wastewater Treatment ◽  
Water Utility ◽  
Water And Wastewater

Nationally, 4% of electricity usage goes towards moving and treating water and wastewater. The energy intensity of the water and wastewater utility sector is affected by many factors including water source, water quality, and the distance and elevation that water must be transported. Furthermore, energy accounts for 10% or more of a utility’s total operating cost, suggesting that energy savings can account for significant cost savings. Better knowledge of where and when energy is used could support strategic energy interventions and reveal opportunities for efficiency. Accordingly, this investigation quantifies energy intensity by process and type, including electricity and natural gas, and explores the time-varying nature of electric energy consumption for potable water distribution using the Austin Water Utility (AWU) in Austin, Texas as a case study. This research found that most of energy consumed by the AWU is for pumping throughout the distribution network (57%) and at lift stations (10%) while potable water treatment accounts for the least (5%). Though the focus is site specific, the methodology shown herein can be applied to other utilities with sufficient data.

scholarly journals Limitations of demand- and pressure-driven modeling for large deficient networks

2017 ◽  
Vol 10 (2) ◽  
pp. 93-98 ◽  
Author(s):  
Mathias Braun ◽  
Olivier Piller ◽  
Jochen Deuerlein ◽  
Iraj Mortazavi
Keyword(s):  
Water Distribution ◽  
Network Models ◽  
Distribution Networks ◽  
Computer Applications ◽  
State Variables ◽  
Modeling Process ◽  
Uniqueness Of The Solution ◽  
Basic Equations ◽  
Physical Phenomena ◽  
Pressure Driven

Abstract. The calculation of hydraulic state variables for a network is an important task in managing the distribution of potable water. Over the years the mathematical modeling process has been improved by numerous researchers for utilization in new computer applications and the more realistic modeling of water distribution networks. But, in spite of these continuous advances, there are still a number of physical phenomena that may not be tackled correctly by current models. This paper will take a closer look at the two modeling paradigms given by demand- and pressure-driven modeling. The basic equations are introduced and parallels are drawn with the optimization formulations from electrical engineering. These formulations guarantee the existence and uniqueness of the solution. One of the central questions of the French and German research project ResiWater is the investigation of the network resilience in the case of extreme events or disasters. Under such extraordinary conditions where models are pushed beyond their limits, we talk about deficient network models. Examples of deficient networks are given by highly regulated flow, leakage or pipe bursts and cases where pressure falls below the vapor pressure of water. These examples will be presented and analyzed on the solvability and physical correctness of the solution with respect to demand- and pressure-driven models.

scholarly journals Technical note: Problem specific variators in a genetic algorithm for the optimization of drinking water networks

2018 ◽  
Author(s):  
Karel van Laarhoven ◽  
Ina Vertommen ◽  
Peter van Thienen
Keyword(s):  
Drinking Water ◽  
Real World ◽  
Water Distribution ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Technical Note ◽  
Practical Implementation ◽  
Utility Level ◽  
Water Utility ◽  
Drinking Water Distribution

Abstract. Genetic algorithms can be a powerful tool for the automated design of optimal drinking water distribution networks. Fast convergence of such algorithms is a crucial factor for successful practical implementation at the drinking water utility level. In this technical note, we therefore investigate the performance of a suite of genetic variators that was tailored to the optimisation of a least-cost network design. Different combinations of the variators are tested in terms of convergence rate and the robustness of the results during optimisation of the real world drinking water distribution network of Sittard, the Netherlands. The variator configurations that reproducibly reach the furthest convergence after 105 function evaluations are reported. In the future these may aid in dealing with the computational challenges of optimizing real world networks.

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