基于混合LSTM-PINN模型预测印度人口转型在生育政策情景下的演变
Forecasting India's Demographic Transition Under Fertility Policy Scenarios Using hybrid LSTM-PINN Model
AI总结：
本研究提出混合LSTM-PINN模型，用于预测印度在不同生育政策情景下的人口转型，揭示政策对人口结构及劳动力影响。
摘要：
人口预测仍然是快速发展的国家如印度在政策规划中的基本挑战，其中生育转型、政策干预和年龄结构动态以复杂方式相互作用。在本研究中，我们提出了一种混合建模框架，将政策感知的生育函数整合到增强长短期记忆网络（LSTM）的物理信息神经网络（PINN）中，以捕捉人口动态中的物理约束和时间依赖性。该模型应用于印度2024至2054年的年龄结构人口，针对三种生育政策情景：维持当前生育下降、更严格的控制和放松的生育促进。通过印度特定的人口指标，包括年龄特定的生育率和死亡率，建立了传输-反应偏微分方程。PINN嵌入了核心人口方程和政策驱动的生育变化，而LSTM层提高了跨十年的长期预测能力。结果显示，生育政策显著影响未来年龄分布、依赖比和劳动力规模。更严格的控制加剧老龄化并减少劳动力参与，而放松的政策支持劳动力增长但增加人口压力。我们的发现表明，混合LSTM-PINN是一种有效的人口预测方法，提供准确性和可解释性。除了方法学创新外，这项工作为印度人口政策辩论提供了可操作的见解，强调了需要平衡的生育干预以确保可持续的社会经济发展。
英文摘要：
Demographic forecasting remains a fundamental challenge for policy planning in rapidly evolving nations such as India, where fertility transitions, policy interventions, and age structured dynamics interact in complex ways. In this study, we present a hybrid modelling framework that integrates policy-aware fertility functions into a Physics-Informed Neural Network (PINN) enhanced with Long Short-Term Memory (LSTM) networks to capture physical constraints and temporal dependencies in population dynamics. The model is applied to India's age structured population from 2024 to 2054 under three fertility-policy scenarios: continuation of current fertility decline, stricter population control, and relaxed fertility promotion. The governing transport-reaction partial differential equation is formulated with India-specific demographic indicators, including age-specific fertility and mortality rates. PINNs embed the core population equation and policy-driven fertility changes, while LSTM layers improve long-term forecasting across decades. Results show that fertility policies substantially shape future age distribution, dependency ratios, and workforce size. Stricter controls intensify ageing and reduce labour force participation, whereas relaxed policies support workforce growth but increase population pressure. Our findings suggest that the hybrid LSTM-PINN is an effective approach for demographic forecasting, offering accuracy with interpretability. Beyond methodological novelty, this work provides actionable insights for India's demographic policy debates, highlighting the need for balanced fertility interventions to ensure sustainable socio-economic development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
