Mostapha Kalami Heris
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Mostapha Kalami Heris has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Alemtsehay G. Seyoum
Department of Civil and Structural Engineering, University of Sheffield, Sheffield S1 3JD, UK
1 shared publication - Mohammadali Geranmehr
Univ. of Sheffield
1 shared publication
Latest Publications
- Open Access
- 4 September 2024
- Published by MDPI AG in Engineering Proceedings
- Vol. 69 (1) , 56
Battle of Water Demand Forecasting: An Optimized Deep Learning Model
Ensuring a steady supply of drinking water is crucial for communities, but predicting how much water will be needed is challenging because of uncertainties. As a part of Battle of Water Demand Forecasting (BWDF), this study delves into the application of Long Short-Term Memory (LSTM) networks for water demand forecasting in a city situated in the northeast of Italy. The focus is on forecasting the demand across ten distinct District Metering Areas (DMAs) over four distinct stages. To enhance the performance of the LSTM model, an evolutionary optimization algorithm is integrated, aiming to fine-tune the model’s hyper-parameters effectively. Results indicate the promising potential of this approach for short-term demand forecasting.
- 4 September 2024
- Published by MDPI AG in Engineering Proceedings
- Vol. 69 (1) , 56
Open AccessBattle of Water Demand Forecasting: An Optimized Deep Learning Model
Ensuring a steady supply of drinking water is crucial for communities, but predicting how much water will be needed is challenging because of uncertainties. As a part of Battle of Water Demand Forecasting (BWDF), this study delves into the application of Long Short-Term Memory (LSTM) networks for water demand forecasting in a city situated in the northeast of Italy. The focus is on forecasting the demand across ten distinct District Metering Areas (DMAs) over four distinct stages. To enhance the performance of the LSTM model, an evolutionary optimization algorithm is integrated, aiming to fine-tune the model’s hyper-parameters effectively. Results indicate the promising potential of this approach for short-term demand forecasting.