基于深度学习的干旱预测方法研究进展

RESEARCH PROGRESS ON DROUGHT PREDICTION METHODS BASED ON DEEP LEARNING

关键词:  深度学习  干旱预测  干旱  作物生产  气象灾害

Key words:  deep learning  drought prediction  drought  crop production  meteorological disasters

摘要:

目的 相比于其他自然灾害，干旱是一种缓慢发展的危害事件，其持续时间长、破坏性大，给农业生产和自然生态环境带来很大影响。预测未来的干旱状况和发展趋势，对于制定科学有效的管理计划以减少灾害造成的损失具有重要意义。方法 文章着重归纳总结当前常用的5种基于深度学习的干旱预测模型及其采用的技术，厘清干旱预测方法的发展过程和最新进展，探索干旱预测领域面临的挑战和机遇，并提出未来干旱预测研究的研究方向。结果 （1）基于深度学习模型的干旱预测方法在均方根误差、偏差、平均绝对误差和准确性等检验指标上均优于传统模型。（2）深度学习方法是有效建立干旱预测模型的工具，能够处理大规模、高维度和复杂的数据。（3）将不同的深度学习模型与各种仿生优化技术、小波分析、传统的物理模型或统计模型融合，能够降低模型预测的不确定性，提高预测的准确性和适应性。（4）基于深度学习的干旱预测模型对数据依赖性强、对计算资源需求高，未得到全面系统的应用。结论 在气候条件频繁变化的情况下，需要不断优化数据采集和监测系统，综合利用气象、农业、水文、生态和社会经济等多源信息，提高干旱预测精度。通过迁移学习、多模型融合、更先进的不确定性建模方法等来完善干旱预测方法，进一步推动深度学习技术在干旱预测领域的深入研究。

Abstract:

Compared to other natural disasters, drought is a slow-developing hazard event with a long duration and great destructiveness, which has a great impact on agricultural production and the natural ecological environment. Predicting the development and retreat process of drought is of significant importance for creating scientific and effective drought response strategies to reduce the losses caused by disasters. Through literature analysis, this paper focused on summarizing five commonly deep learning-based drought prediction models and their technologies, clarifying the development process and latest progress of drought prediction methods, exploring challenges and opportunities in the field of drought prediction, and proposing research directions for future drought prediction research. The results were showed as follows. Deep learning-based drought prediction models had lower root mean square error, bias, mean absolute error, and higher accuracy compared to traditional models. Deep learning could be a valuable tool for drought prediction because it could handle large, complex, and high-dimensional data. Combining different deep learning models with various bionic optimization techniques, wavelet analysis, traditional physical models or statistical models could reduce the uncertainty of model predictions and improve the accuracy and adaptability of predictions. However, a deep learning-based drought prediction model with a strong dependence on data requires large amounts of computing resources, and had not been applied comprehensively and systematically. Therefore, data acquisition and monitoring systems need to be continuously optimized in the face of frequent changes in climatic conditions. Comprehensive utilization of multi-information from various fields, such as meteorology, agriculture, hydrology, ecology, and socio-economics, has been found to enhance drought prediction accuracy. Further research is needed to promote the deep learning technology applied in the field of drought forecasting by incorporating transfer learning, multi-model fusion, and uncertainty modeling methods.