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Deep hybrid network with additive attention for accurate population forecasting.

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Title: Deep hybrid network with additive attention for accurate population forecasting.
Authors: Yar, Hikmat1 (AUTHOR), Hussain, Adnan1 (AUTHOR), Khan, Zulfiqar Ahmad1,2 (AUTHOR), Kim, Min Je1 (AUTHOR), Baik, Sung Wook1 (AUTHOR) sbaik@sejong.ac.kr
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Apr2026, Vol. 30 Issue 4, p2737-2754. 18p.
Subjects: Population forecasting, Machine learning, Demography, Artificial intelligence, Convolutional neural networks, Long short-term memory
Abstract: Population forecasting is crucial for informed decision-making and strategic planning in government and business, as it supports effective resource allocation, infrastructure development, and service provision to meet future demands. However, accurate population forecasting for small regions is challenging due to complex demographic processes, data sparsity, and the nonlinear nature of population growth. Despite advancements in time series forecasting methods, limited research has applied machine learning to small-area population forecasting, highlighting the need for models capable of capturing complex demographic trends to improve accuracy. To tackle these limitations, we propose a dual-stream architecture combining convolutional neural network (CNN) and bidirectional LSTM (BiLSTM) layers with additive attention mechanisms (Dual-CBA) for small-area population forecasting. The first stream utilizes CNN layers for spatial feature extraction, while the second stream employs a multilayered BiLSTM network to model temporal dependencies, capturing demographic trends and changes over time. The integration of additive attention allows the model to focus on relevant features in both spatial and temporal dimensions, enhancing its ability to learn complex demographic patterns and improve forecasting accuracy. The Dual-CBA is evaluated on five datasets namely Australia, New Zealand, Japan, United States, and South Korea. Results demonstrate that the proposed model achieves superior performance in terms of MAPE and MedAPE compared to the baselines. The model performance is also examined for 5- and 10-year population forecasts, showing optimal performance across these timeframes. Additionally, we also use the XAI method LIME to assess feature importance by region, providing insights into the key factors influencing population projections. [ABSTRACT FROM AUTHOR]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192586155
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Deep hybrid network with additive attention for accurate population forecasting.
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  Data: <searchLink fieldCode="DE" term="%22Population+forecasting%22">Population forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Demography%22">Demography</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink>
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  Data: Population forecasting is crucial for informed decision-making and strategic planning in government and business, as it supports effective resource allocation, infrastructure development, and service provision to meet future demands. However, accurate population forecasting for small regions is challenging due to complex demographic processes, data sparsity, and the nonlinear nature of population growth. Despite advancements in time series forecasting methods, limited research has applied machine learning to small-area population forecasting, highlighting the need for models capable of capturing complex demographic trends to improve accuracy. To tackle these limitations, we propose a dual-stream architecture combining convolutional neural network (CNN) and bidirectional LSTM (BiLSTM) layers with additive attention mechanisms (Dual-CBA) for small-area population forecasting. The first stream utilizes CNN layers for spatial feature extraction, while the second stream employs a multilayered BiLSTM network to model temporal dependencies, capturing demographic trends and changes over time. The integration of additive attention allows the model to focus on relevant features in both spatial and temporal dimensions, enhancing its ability to learn complex demographic patterns and improve forecasting accuracy. The Dual-CBA is evaluated on five datasets namely Australia, New Zealand, Japan, United States, and South Korea. Results demonstrate that the proposed model achieves superior performance in terms of MAPE and MedAPE compared to the baselines. The model performance is also examined for 5- and 10-year population forecasts, showing optimal performance across these timeframes. Additionally, we also use the XAI method LIME to assess feature importance by region, providing insights into the key factors influencing population projections. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – TitleFull: Deep hybrid network with additive attention for accurate population forecasting.
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              Text: Apr2026
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