Droughts can cause significant damage to agriculture and water resources leading to severe economic losses. One of the most important aspects of drought management is to develop useful tools to forecast drought events, which could be helpful in mitigation strategies. The recent global trends in drought events reveal that climate change would be a dominant factor in influencing such events. The present study aims to understand this effect for the New South Wales (NSW) region of Australia, which has suffered from several droughts in recent decades. The understanding of the drought is usually carried out using a drought index, therefore the Standard Precipitation Evaporation Index (SPEI) was chosen as it uses both rainfall and temperature parameters in its calculation and has proven to better reflect drought. The drought index was calculated at various time scales (1, 3, 6, and 12 months) using a Climate Research Unit (CRU) dataset. The study focused on predicting the temporal aspect of the drought index using 13 different variables, of which eight were climatic drivers and sea surface temperature indices, and the remainder were various meteorological variables. The models used for forecasting were an artificial neural network (ANN) and support vector regression (SVR). The model was trained from 1901–2010 and tested for nine years (2011–2018), using three different performance metric scores (coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The results indicate that ANN was better than SVR in predicting temporal drought trends, with the highest R² value of 0.86 for the former compared to 0.75 for the latter. The study also reveals that sea surface temperatures and the climatic index (Pacific Decadal Oscillation) do not have a significant effect on the temporal drought aspect. The present work can be considered as a first step, wherein we only study the temporal trends, towards the use of climatological variables and drought incidences for the NSW region.

中文翻译：

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使用机器学习方法对澳大利亚新南威尔士州的时态水文干旱指数进行预测

干旱会严重破坏农业和水资源，导致严重的经济损失。干旱管理的最重要方面之一是开发有用的工具来预测干旱事件，这可能有助于缓解策略。全球干旱事件的最新趋势表明，气候变化将是影响此类事件的主要因素。本研究旨在了解对澳大利亚新南威尔士州（NSW）地区的这种影响，该地区最近几十年来遭受了几次干旱。通常使用干旱指数来了解干旱，因此选择标准降水蒸发指数（SPEI），因为它在计算中使用了降雨和温度参数，并被证明可以更好地反映干旱。使用气候研究单位（CRU）数据集在不同的时间尺度（1、3、6和12个月）计算干旱指数。该研究着重于使用13个不同的变量来预测干旱指数的时间方面，其中8个是气候驱动因素和海表温度指数，其余是各种气象变量。用于预测的模型是人工神经网络（ANN）和支持向量回归（SVR）。该模型从1901年至2010年进行了训练，并使用三个不同的绩效指标得分（测定系数（R 其中八个是气候驱动因素和海表温度指数，其余是各种气象变量。用于预测的模型是人工神经网络（ANN）和支持向量回归（SVR）。该模型从1901年至2010年进行了训练，并使用三个不同的绩效指标得分（测定系数（R 其中八个是气候驱动因素和海表温度指数，其余是各种气象变量。用于预测的模型是人工神经网络（ANN）和支持向量回归（SVR）。该模型从1901年至2010年进行了训练，并使用三个不同的绩效指标得分（测定系数（R²），均方根误差（RMSE）和平均绝对误差（MAE）。结果表明，ANN在预测暂时干旱趋势方面优于SVR，前者的最高R ²值为0.86，后者为0.75。研究还表明，海表温度和气候指数（太平洋年代际涛动）对暂时干旱没有明显影响。当前的工作可以视为第一步，其中我们仅研究针对新南威尔士州气候变量和干旱发生率的时间趋势。