همایش بین المللی هوش مصنوعی

صفحه اصلی / اولین همایش بین المللی هوش مصنوعی

Time Series Algorithms for Predicting Monthly Water Consumption

نویسندگان :

Mohsen Piri¹ Babak Nouri-Moghaddam² Abbas Mirzaei³

1- Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran 2- Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran 3- Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran

کلمات کلیدی :

LSTM model،water consumption prediction،ARIMA model.

چکیده :

This research paper reviews various time series algorithms for predicting monthly water consumption. The authors explore a range of machine learning models, including LSTM, RNN, GRU, BiLSTM, and BiGRU, alongside traditional methods such as ARIMA, to forecast water demand. The study evaluates the advantages and limitations of each approach, considering factors like accuracy, computational complexity, and data quality. A central focus is on enhancing water resource management by providing accurate predictions to anticipate shortages and optimize resource allocation. The paper also addresses the challenges in this field and suggests directions for future research.