Impacts of Weather Variables on Urban Water Demand at Multiple Temporal Scales

Makpiboon, Chinnapan

doi:10.21660/2020.67.5758

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…In the second transformation based on weather, four predictors were available, namely T, R, W and H. However, only T and R were significant in every model. In addition, the p-values of T were smaller than those of R. Makpiboon et al (2020) studied the total water demand in three provinces (Bangkok, Nonthaburi and Samutprakarn) in Thailand using three different time scales (daily, monthly and seasonal) based on T and R. They found that both weather predictors were significant with the p-values of T smaller than those of R. Thus, our results agreed with their research results. Finally, 12 predictors (months of the year) were selected in the third transformation based on calendar period.…”

Section: Model Parameterssupporting

confidence: 88%

Impact of COVID-19 on monthly water consumption on a tropical tourism island: case study of Phuket (Thailand)

et al. 2021

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Phuket is a tropical island in Thailand that is famous for tourism. The COVID-19 pandemic resulted in the number of tourists reducing to almost zero. Since tourism contributes around one-half of the gross provincial product of Phuket, the impact was so severe that even the numbers of people employed and registered as locals decreased. Analysing the data from January 2015 to March 2021, we found that the total, residential and non-residential monthly consumptions dropped significantly after Thailand's State of Emergency was declared in March 2020. Unlike other studies that reported residential consumption increasing when people are required to stay home for a prolonged period, Phuket's residential consumption decreased by more than 10% from the pre-COVID-19 level, possibly due to the drop in peer-to-peer accommodation bookings. To study the impact on consumption in detail, we modelled using cascade regression analysis by dividing the predictors into three groups, namely socioeconomics, weather and calendar period. The results showed that the number of guest arrivals was the most statistically significant in all types of consumption and should be used as a predictor for water demand forecasting models in tourism areas.

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Section: Model Parameterssupporting

confidence: 88%

Impact of COVID-19 on monthly water consumption on a tropical tourism island: case study of Phuket (Thailand)

et al. 2021

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“…Many factors can directly or indirectly influence BWD, including the variables of weather such as rainfall, temperature, and air quality, as well as other factors such as class schedules, weekends, and national holidays. Climate variables, in particular, have been frequently used as inputs to multivariate statistical models and machine learning approaches for modeling and predicting urban water time series [16][17][18][19].…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Bathwater Demand Forecasting for Shared Shower Rooms in Smart Campuses Using Machine Learning Methods

Zhang

Yang

et al. 2022

Water

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Water scarcity is a growing threat to humankind. At university campuses, there is a need for shared shower room managers to forecast the demand for bath water accurately. Accurate bath water demand forecasts can decrease the costs of water heating and pumping, reduce overall energy consumption, and improve student satisfaction (due to stability of bath water supply and bathwater temperature). We present a case study conducted at Capital Normal University (Beijing, China), which provides shared shower rooms separately for female and male students. Bath water consumption data are collected in real-time through shower tap controllers to forecast short-term bath water consumption in the shower buildings. We forecasted and compared daily and hourly bath water demand using the autoregressive integrated moving average, random forests, long short-term memory, and neural basis expansion analysis time series-forecasting models, and assessed the models’ performance using the mean absolute error, mean absolute percentage error, root-mean-square error, and coefficient of determination equations. Subsequently, covariates such as weather information, student behavior, and calendars were used to improve the models’ performance. These models achieved highly accurate forecasting for all the shower room areas. The results imply that machine learning methods outperform statistical methods (particularly for larger datasets) and can be employed to make accurate bath water demand forecasts.

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“…Additional attention should be paid to external factors that may influence water demand, e.g., weather, season, days of the week, and public holidays [29,30]. Recent research has shown that machine learning has much faster and more accurate results compared with traditional statistical models.…”

Section: Introductionmentioning

confidence: 99%

“…Recent research has shown that machine learning has much faster and more accurate results compared with traditional statistical models. Among the most common input data for machine learning models that aim to predict water time series were climate values [30]. A real challenge to obtaining highly accurate water demand prediction is choosing the relevant algorithms for data processing [31].…”

Section: Introductionmentioning

confidence: 99%

Advanced Strategies for Monitoring Water Consumption Patterns in Households Based on IoT and Machine Learning

Arsene

Predescu

Pahonțu

et al. 2022

Water

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Water resource management represents a fundamental aspect of a modern society. Urban areas present multiple challenges requiring complex solutions, which include multidomain approaches related to the integration of advanced technologies. Water consumption monitoring applications play a significant role in increasing awareness, while machine learning has been proven for the design of intelligent solutions in this field. This paper presents an approach for monitoring and predicting water consumption from the most important water outlets in a household based on a proposed IoT solution. Data processing pipelines were defined, including K-means clustering and evaluation metrics, extracting consumption events, and training classification methods for predicting consumption sources. Continuous water consumption monitoring offers multiple benefits toward improving decision support by combining modern processing techniques, algorithms, and methods.

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Impacts of Weather Variables on Urban Water Demand at Multiple Temporal Scales

Cited by 3 publications

References 7 publications

Impact of COVID-19 on monthly water consumption on a tropical tourism island: case study of Phuket (Thailand)

Impact of COVID-19 on monthly water consumption on a tropical tourism island: case study of Phuket (Thailand)

Short-Term Bathwater Demand Forecasting for Shared Shower Rooms in Smart Campuses Using Machine Learning Methods

Advanced Strategies for Monitoring Water Consumption Patterns in Households Based on IoT and Machine Learning

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