Journal of Global Resources
Volume 5 (02)
July 2019
ISSN: 2395-3160 (Print), 2455-2445 (Online)

Page 91-97

13SCOPE OF MACHINE LEARNING IN WATER CONSUMPTION AND POPULATIONFORECASTING
Noopur Rathore1, Rajeshwari Acharya2 and M. P. Punia2
B.Tech Student, Information Technology, Swami Keshvanand Institute of Technology,
Management and Gramothan, Jaipur (Rajasthan), India
2
JRF, Department of Remote Sensing, Birla Institute of Scientific Research, Jaipur, India
2Head, Department of Remote Sensing,
Birla Institute of Technology, Jaipur (Rajasthan), India
Email: nprr1305@gmail.com, rajeacharya@yahoo.com, punia.rsd@gmail.com
1Abstract: Water is of paramount importance for the existence of life on Earth. The causes ofwater depletion are both natural and anthropogenic. On Earth, amount of freshwater has
remained persistent over span but the population has mushroomed. Therefore strive forfreshwater intensifies day by day. Proper management and forecasting are required forbetter and effective water usage plans. Water demand and population forecasting are themajor parameters for an Urban Water Management. Machine learning is among the bestknown techniques for such forecasting. Machine learning is a data analytics techniquethat provides machine the potential to learn without being comprehensively
programmed. Unlike the traditional methods of demand forecasting that were notsuitable for historical unstructured and semi structured data, machine learning takes
into account or has the capabilities for analysing such data. This paper focuses onsupervised machine learning techniques i.e. Artificial Neural Networks, Linear
Regression and ARIMA modelling for assessing water consumption and population
forecasting for urban water management.
Key words: Water Consumption, Population Forecasting, Demand Forecasting, Machine
Learning, Artificial Neural Network, Linear Regression
Introduction
On Earth freshwater constitutes a very small fraction of the total available water andtherefore need for sustainability of this natural resource is required. The water crisis iscommonly seen and many steps are already been taken to avoid this problem but some arestill uncertain. In today’s data-driven world, massive quantities of data can be analyzed using
machine learning (ML) with faster and more accurate results. ML is a great replacement forearlier statistical techniques. It can be done using three approaches that are Supervised
Learning, Unsupervised Learning, semi-supervised and Reinforcement Learning.
Existing machine learning works in the water and wastewater industry includes
investigations into potential outbreaks through the water system, environmental
impacts of wastewater treatment and disposal and estimates the effluent quality of
wastewater treatment plants. Dexterity to handle data, ML proves to be an ideal tool
for water asset management. Water consumption is a portion of water that is being
withdrawn but never returned to the source while population forecasting is a field ofdemography that deals with projecting future population. Therefore interrelationshipbetween them is used for predictive analysis. Described the advances in urban water
demand modelling over the past three decades by considering parameters like water
demand, climate, population, reservoirs, pumping stations, precipitation, and temperature
and water price with the help of interactions across spatial and temporal scales.
Water consumption depends on several factors like topography, industrial
presence and infrastructure, climatic conditions, quality of water, socio-economic
elements, population growth and density, pressure in the water distribution system,water prices, policies of water meters, and efficiency of waterworks administrations.
This study also proposes a time series analysis of water consumption. Described a time91

series model to forecast daily water consumption considering four parameters namely trend,seasonality, climatic correlation, and autocorrelation. This paper provides a frame ofreference for machine learning algorithms for the use of practitioners who want to makedecisions and for researchers who want to contribute in this field and moreover for the end
users it provides a variety of new knowledge.
Objective
This paper focuses on the scope of machine learning algorithms (artificial neural networks,linear regression, and ARIMA model) for population growth and water consumption
forecasting considering different parameters and hence assessing the interrelationshipbetween the two. It also evaluates the applicability and accuracy of forecast models.
Assumptionsi.
Data published by the government bodies are reliable.
ii.
Open source software has been used for validation.
Limitationsi.
Limited access to India’s historical water consumption data.
ii.
Focuses on Toronto city’s water consumption data.
Methodology
Data and method
A model is built on Toronto city’s water consumption and population data. Here, water
consumption data from the year 2000-2015 and population census data from 2001-2016 isused (data source: [8]). RStudio software (version 1.2.1335) has been used. Figure 1 given
below depicts the workflow process of data analysis.
Figure 01: Work Flow Process

Algorithm and Analysis
In data collection information from different sources was gathered and evaluated, followed bydata modification which included remoulding of data in such a scheme that it can be feasible
to work with by formatting and cleaning. After that data visualization was applied, in whichdata was modified into some form of plots and inspected further. By visualizing the data from2005-2015, it was found that total residential usage was maximum in ward 41 i.e.
Scarborough-Rouge River and total commercial usage was maximum in ward 27 i.e. Toronto
Centre Rosedale. Average residential usage was maximum in ward 20 i.e. ScarboroughSouthwest and average commercial usage was maximum in ward 11 i.e. York South West.
Ward 27 has consistently consumed the most water commercially every year from 2005 to2015. Figure 2 shows the total residential usage by ward and total commercial usage by
ward from 2005-2015; from this plot minimum and maximum water consuming wards can beidentified.
Figure 02: Total Residential & Commercial Usage by Ward 2005-201592Linear Regression
Linear regression is a supervised machine learning technique, used for predictive analysis ofdependent variables based on independent variables. Water demand forecasting year-wise
was done using linear regression. The dependent variable for this algorithm was totalconsumption and the independent variable was year. Therefore equation so formed was“Total Consumption ~ Year” with slope and intercept as -4581081.66 and 9535168992.29
respectively.
Yestimated = θ1x + θ2
Where Yestimated is labels to data, x is training data, θ1 is slope or gradient: -4581081.66
and θ2 is intercept or value of y when x is 0.9535168992.29. Mean squared error (MSE) is themean of the squared errors and the root mean square error (RMSE) is the square root of themean of the squared errors and is given by:”√(1/n)∑(yi - yi’)2”. For this study RMSE value
hence obtained was 0.7. Figure 3 illustrates the forecasted water consumption from 20172025.
Figure 03: Forecasted Water Consumption

Artificial Neural Networks
Water demand prediction ward-wise was done using ANN, which is a non-linear model. Feedforward ANN and feedback ANN are its two types. The configuration used here is,f(x)=K(∑iwigi(x))
Where, K is activation function, gi(x) is collection of functions, wi is weight assigned to aconnection. Here, feed forward, ANN is used with a single hidden node. Initially, datanormalization was achieved using min-max normalization and then the model was built using
the equation “Total Consumption of ward1 ~ Year+ Total Count” (Ward1 is used as adummy). Total consumption was a dependent variable on independent variables year andtotal count. The accuracy of the model is quite evident by the plot, with an error of just
0.06204 and total steps 134. Output on the last node i.e. node 4 was also shown using thesigmoid function which was equal to the predicted value of total consumption.
Sigmoid function = 1/(1 + e-x)
Where, x is input to that node. Figure 4 represents the artificial neural network model of ward 1prediction.
Figure 04: Artificial Neural Network model for Ward 1 prediction

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ARIMA Model
Ward-wise water demand forecasting was done using the ARIMA (Auto-Regressive
Integrated Moving Average) model, it is a type of statistical model used for forecasting using
time series dataset. ARIMA (p,d,q) was used with the appropriate value of parameters toform a specific model. When two out of the three terms are zeros, the model is based on
non-parameter releasing ‘AR’, ‘I’ or ‘MA’. Water demand forecasting on ward 42 was doneusing the ARIMA model as it had the highest residential usage from the year 2011-2015. At
the outset, data was converted into time series and then forecasted. Figure 5 given below
shows the forecasted water consumption in ward 42.
Figure 05: Forecasted water consumption in ward 42

Population Forecasting
Similar to water demand ward-wise forecasting, population projection was also done using
the ARIMA model. Data initially was available with a gap of 5 years. Using the population
estimation formula, the population per year was calculated and then converted into timeseries and forecasted with parameters (1,2,0).
Pestimated= P1 + (n/N) (P2 – P1)
Where, Pestimated is the population estimate for a given year, n is the number of months
from P1 census to the date of estimate, N is the number of months between census periods,
P2 is the last census taking, and P1 is the second to last census taking. Figure 6 portrays theexpected population of Toronto from 2017-2025.
Figure 06: Forecasted Population of Toronto

Lastly, projected water use in litres per capita per day (l/c/d) was calculated using residential
water usage and population by the formula given below:
l/c/d = (WC * 1000) / (D * P)
Where, WC is Total water consumption during the assessment period, D is Number ofdays, P is Total number of the served population. Similar to the previous implementation ofARIMA model l/c/d trend of Toronto was also forecasted and represented as shown in figure
7 given below:94

Figure 07: Forecasted water consumption l/c/d trend of Toronto

When forecasted water consumption and forecasted population were drawn on a single plot
then the graph so obtained depicted the relationship between the two, figure 8 drafts thesame. From the graph thus obtained, certain conclusions were drawn which are mentioned in
the result section.
Figure 08: Relationship between Forecasted Water Consumption and Forecasted
Population

Results and Conclusion
This paper presented the scope of machine learning in urban water management and aims
to bring advance knowledge and understanding about the same to develop methodologies
and tools for further study. This paper culminated from observations that, water use in liters
per capita per day went down gradually which is below the standard water consumption i.e.
150 l/c/d, it signifies that water supply w.r.t. to population is not up to the required quantity.
From the relationship plot between forecasted water consumption and population, it isobserved that time around 2024 will be a very critical one since the population will increase
and water supply will decrease; as depicted in the plot, two curves cut each other. This is amatter of concern and should be kept in mind by the government and steps should be taken
accordingly to avoid such conditions. Moreover, it is proven by the study that ML algorithms
like ANN, linear regression and ARIMA modeling outperforms on such datasets as theminimal error was obtained.
Further demand forecasting helps in resource management for the government, somany more machine learning techniques can be used to predict and forecast water
consumption and population to attain sustainable life. This paper permits a reliable forecastof water demand which in turn can also help in optimizing energy costs, treatment, storage
and distribution of water. Moreover, it can be beneficial for analysts in the field of hydrology
and water resource management when combined with artificial intelligence and othercognitive technologies to give remarkable results.

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Literature Review
S.No.
1

Objective
This paper presents the machine learning water
demand forecasting models with more accurate
predictions as compared to traditional strategies.
This paper presents combining time series
clustering, for the identification of daily urban
water demand patterns, and Support Vector
Regression for performing a short term forecast.
This paper reviews the literature on urban water
demand forecasting from 2000 to 2010 to identify
methods and models necessary for specific water
utility decision-making problems.
This paper focuses on real-time pump schedulingin a water distribution system using an optimizer.

Parameter
weather, seasonality,water demand

Methodology
Forecasting methodology involves neural networks, random
forests, support vector machines, and k-nearest neighbours.

Data retrieved fromthe SCADA system

This paper reviews the soft computing methods ofwater consumption forecasting between 2005 and2015.
Water demand forecasting using a combination oftechniques such as multilayer perceptrons,
support vector machines, extreme learning
machines, random forests, and adaptive neuralfuzzy inference systems.
The goal was to avoid overwhelming operatorswith voluminous raw data and present thesolutions to link and interpret data streams.

Population, water
consumption, climate

The approach consists of clustering together daily demand
patterns, represented by the volume of water delivered at eachhour, and aiming at identifying a prediction model, based on theSupport Vector Regression.
The methodology included forecasting with qualitative methods,
univariate time series analysis, time series regression models,decision support system, artificial neural networks, and compositemodels.
The methodology involves the use of adaptive ANN training as analternative to the back propagation method and making use of amulti-method optimization algorithm as an alternative to theGenetic algorithm.
The methodology includes artificial neural networks (ANNs), fuzzyand neuro-fuzzy models, support vector machines, metaheuristics,
and system dynamics.
Presented the methodology of committee machines for creating a
predictive model for urban water demand.

8Enhancing water system using sensor
technologies, firmware application, big data tools,
and M2M technology

Water quality, flow,pressure

9A Model Conditional Processor (MCP) application
is presented to assess the predictive uncertainty inwater demand forecasting.
To forecast water consumption for
Melbourne, Australia a time series model isformulated which relies on past demand data.

Water demand, water
distribution system

234567

10Leakage, water
demandSensor data, flowdata

Climate, water
demandBig data from water
and wastewaterindustries

Trend, seasonality,
climatic correlation,
autocorrelation

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The methodology used here involves data analytics. Data
analytics is used here to determine probable future outcomes toidentify relationships and detect multivariate data excursions fromthe normal ones.
It uses sensor technologies and firmware applications, big data
analytics tools and embedded Machine-to-Machinecommunications for collecting data enabling better information to
be provided to the human operators that were previously been
manually collected and rarely evaluated.
The method involves converting historical observation and thecorresponding forecasted values into normal space for theconditional distribution of the real values to the forecasted ones.
The methodology adopted was based on time series analysis inwhich daily water consumption was taken as the sum of baseconsumption and seasonal consumption.

Reference
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