Enhancing Water Demand Forecasting: Leveraging LSTM Networks for Accurate Predictions

This study aims to create a reliable water-demand forecasting system using Long Short-Term Memory networks. The model integrates hourly water demands from 10 District Metered Areas of a Water Distribution Network in northeast Italy and weather data, handling missing values with LSTM-based data imput...

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Detaylı Bibliyografya
Asıl Yazarlar:	Fatemeh Boloukasli ahmadgourabi, Melica Khashei Varnamkhasti, Morad Nosrati Habibi, Niuosha Hedaiaty Marzouny, Rebecca Dziedzic
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	MDPI AG 2024-09-01
Seri Bilgileri:	Engineering Proceedings
Konular:	water demand forecasting LSTM networks missing values cyclical patterns feature engineering techniques
Online Erişim:	https://www.mdpi.com/2673-4591/69/1/120
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Enhancing Water Demand Forecasting: Leveraging LSTM Networks for Accurate Predictions

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