Assessment of ERA5-Land Data in Medium-Term Drinking Water Demand Modelling with Deep Learning

Drinking water demand modelling and forecasting is a crucial task for sustainable management and planning of water supply systems. Despite many short-term investigations, the medium-term problem needs better exploration, particularly the analysis and assessment of meteorological data for forecasting...

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Veröffentlicht in:Water (Basel) 2023-04, Vol.15 (8), p.1495
Hauptverfasser: Dhawan, Pranav, Dalla Torre, Daniele, Zanfei, Ariele, Menapace, Andrea, Larcher, Michele, Righetti, Maurizio
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container_issue 8
container_start_page 1495
container_title Water (Basel)
container_volume 15
creator Dhawan, Pranav
Dalla Torre, Daniele
Zanfei, Ariele
Menapace, Andrea
Larcher, Michele
Righetti, Maurizio
description Drinking water demand modelling and forecasting is a crucial task for sustainable management and planning of water supply systems. Despite many short-term investigations, the medium-term problem needs better exploration, particularly the analysis and assessment of meteorological data for forecasting drinking water demand. This work proposes to analyse the suitability of ERA5-Land reanalysis data as weather input in water demand modelling. A multivariate deep learning model based on the long short-term memory architecture is used in this study over a prediction horizon ranging from seven days to two months. The performance of the model, fed by ground station data and ERA5-Land data, is compared and analysed. Close-to-operative forecasting is then presented using observed data for training and ERA5-Land dataset for testing. The results highlight the reliability of the proposed architecture fed by ERA5-Land data for different time horizons. In particular, the ERA5-Land shows promising performance as input of the multivariate machine learning forecasting model, although some meteorological biases are present, which can be improved, especially in close-to-operative application with bias correction techniques. The proposed study leads to practical implications in the use of regional climate model outputs to support drinking water forecasting for sustainable and efficient management of water distribution systems.
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subjects Algorithms
Bias
Case studies
Climate change
Climate models
Computer architecture
Datasets
Deep learning
Drinking water
Forecasting
Ground stations
Humidity
Long short-term memory
Machine learning
Management
Meteorological data
Multivariate analysis
Neural networks
Precipitation
Rain
Real property
Sustainability management
Time series
Valuation
Water
Water conveyance
Water demand
Water distribution
Water distribution systems
Water engineering
Water management
Water shortages
Water supply
Water supply systems
Weather
Weather forecasting
title Assessment of ERA5-Land Data in Medium-Term Drinking Water Demand Modelling with Deep Learning
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