Centre for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, New South Wales 2007, Australia.
Centre for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, New South Wales 2007, Australia; Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209, Neungdongro Gwangjin-gu, Seoul 05006, Republic of Korea; Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
Sci Total Environ. 2021 Feb 10;755(Pt 2):142638. doi: 10.1016/j.scitotenv.2020.142638. Epub 2020 Oct 2.
Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks.
旱情的长期预测对于预警系统和风险管理策略至关重要。机器学习算法的应用已被证明对旱情预测有益。然而，由于气候变化的影响和旱情评估的复杂性，长期预测仍然是一个挑战。深度学习技术的兴起可以解决这个问题，本工作旨在使用堆叠长短期记忆（LSTM）架构来预测常用的旱情指标，即标准降水蒸发指数。然后将模型应用于澳大利亚新南威尔士州，以水文气象和气候变量作为预测因子。使用气候研究单位的多元插值网格在每月尺度上计算该指数，以气象变量作为预测因子。该架构使用 1901-2000 年的数据进行训练，并在 2001-2018 年的数据上进行测试。然后在 1 个月到 12 个月的时间范围内对预测结果进行预测。根据旱情特征，如旱情强度、旱情起始时间、空间范围和旱情月份数，对预测结果进行分析，以阐明这些特征如何提高对旱情预测的理解。模型对旱情强度的预测能力使用了两个统计指标，即决定系数（R）和均方根误差。使用威胁分数技术检查旱情月份数的变化。研究结果表明，堆叠 LSTM 模型可以在短期和长期预测中有效预测。这些发现对政府机构至关重要，并可以进一步测试，以了解所提出架构在较短时间尺度（从几天到几周）的预测能力。
