Artificial Intelligence Approaches for Urban Water Demand Forecasting: A Review

Muhammad, Abdullahi Uwaisu; Li, Xiaodong; Feng, Jun

doi:10.1007/978-3-030-32388-2_51

Cited by 11 publications

(3 citation statements)

References 107 publications

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“…Muhammad, and Feng investigated several artificial intelligence techniques such as support vector machine, artificial neural networks, fuzzy logic, and extreme learning machines as well as hybrid models and Autoregressive Integrated Moving Average (ARIMA) in urban water demand forecasting. They concluded that artificial intelligence methods showed superiority especially Artificial Neural Networks for short-term water demand forecasting [24].…”

Section: Related Workmentioning

confidence: 99%

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

2020

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In recent years, scarce water resources became one of the main problems that endanger human species existence and the advancement of any nation. In this research, smart water meters were implemented, distributed, and installed in a regional area in Cairo while data were collected at uniform intervals then sent to the cloud instantly. The solution paradigm uses an Internet of Things (IoT) based on micro-services and containers. The design incorporates real-time streaming and infrastructure performance optimization to store data. A second layer to analyze the acquired data was used to model water consumption using Long Short-Term Memory (LSTM). The designed LSTM is validated and tested to be utilized in the forecast of future water demand. Moreover, two alternative machine learning methods, namely Support Vector Regression and Random Forest commonly utilized in time series forecasting applications, were used for a comparative analysis of which LSTM has proven to be superior. The proper integration of the system elements is the key to the proposed system success. Based on the success of the designed system, it can be applicable on a national scale. That can enable the optimal management of consumers' demand and improve water infrastructure utilization. The proposed paradigm presents a testbed for various scenarios that can be used in water resources management.

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Section: Related Workmentioning

confidence: 99%

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

2020

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“…From a "modellistic" point of view, the AutoRegressive Integrated Moving Average (ARIMA) typology of models is well established, having been applied in the field of water demand forecasting for a long time [8][9][10][11]. This is justified by the fact that the model follows the trend at different time scales.…”

Section: Introductionmentioning

confidence: 99%

Drinking Water Tank Level Analysis with ARIMA Models: A Case Study

Guarnaccia

Longobardi

Mancini

et al. 2020

The 4th EWaS International Conference: Valuing the Water, Carbon, Ecological Footprints of Human Activities

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The operational management of tanks for urban water distribution networks is usually a critical element due to the dynamic nature of the water demand and the age of the distribution networks themselves. Today, in a context of water resource scarcity, optimal management is a key point for the sustainable management of urban systems. For this purpose, it is useful to implement predictive tools, able to provide short-term forecasts to inform urban water managers on the most suitable procedure to be applied in the case of routine or critical events. A possible approach is to use autoregressive integrated moving average (ARIMA) models, which combine the autoregression and the moving average approaches, with the possibility to work on a differenced series of the data. They can further embed a seasonal- component (Seasonal ARIMA models), to account for possible periodic patterns in the observed data. In this study, the data of water levels measured from May 2018 to 10 January 2019 in a water storage tank in the area of Benevento, Campania region (Italy), were considered as a case study. The standard ARIMA techniques were applied to find the best model for this dataset, according to “Deviance Information Criterion” (DIC) and “Bayesian Information Criterion” (BIC) optimization. The results are discussed, shedding light on the behaviour of the time series with reference to the management of the infrastructure and the dataset. The residual analysis, carried out to check if the autocorrelation was still present and if the residuals were normally distributed, revealed a narrow distribution. Small values were found throughout the dataset, except for a few periods, corresponding to the imputed data. This application represents a preliminary step of more detailed research that will be carried out to detect the best model for forecasting tank levels for the case study to help to manage the urban water supply.

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“…En el trabajo de revisión titulado "Enfoques de inteligencia artificial para el pronóstico de la demanda de agua urbana" Muhammad et al (2019) realiza un análisis de diferentes enfoques de IA e híbridos, demostrando la utilidad y las aplicaciones de cada modelo como solución para la previsión de la demanda de agua. La revisión muestra que no existe un único modelo híbrido o de inteligencia artificial que parezca ser el mejor; asimismo, la IA se puede aplicar con éxito para la estimación de dichas predicciones.…”

Section: Gestión Inteligente De Las Redes De Abastecimientounclassified

Metodologías epidemiológicas de análisis de datos para la operación y gestión de redes de abastecimiento urbano de agua

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Artificial Intelligence Approaches for Urban Water Demand Forecasting: A Review

Cited by 11 publications

References 107 publications

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

Drinking Water Tank Level Analysis with ARIMA Models: A Case Study

Metodologías epidemiológicas de análisis de datos para la operación y gestión de redes de abastecimiento urbano de agua

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