Abstract This paper presents a new hybrid model, called ENN-SA, for spatiotemporal drought prediction. In ENN-SA, an Elman neural network (ENN) is conjugated with simulated annealing (SA) optimization and support vector machine (SVM) classification algorithms for the standardized precipitation index (SPI) modeling at multiple stations. The proposed model could be applied to predict SPI at different time scales in a meteorology station with lack of data through the intelligent use of SPI series of the nearby stations as the model inputs. The capability of the hybrid model for multi-station prediction of meteorological drought was examined through the cross-validation technique for Kecioren station in Ankara Province, Turkey. To this end, the SPI-3, SPI-6, and SPI-12 at the station were modeled using the same indices of five nearby stations. In the first step, SVM was trained using different kernels in order to generate and classify a set of plausible multi-station prediction scenarios. Then, ENN was used to regress the SPI series at each scenario and finally, the SA component of the integrated model was utilized to improve the ENN efficiency. Various error and complexity measures were used to detect the models’ performance. The results showed the ENN-SA is promising and efficient for multi-station SPI prediction.

中文翻译：

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ENN-SA：一种用于多站干旱预测的新型神经退火模型

摘要 本文提出了一种新的混合模型，称为ENN-SA，用于时空干旱预测。在 ENN-SA 中，Elman 神经网络 (ENN) 与模拟退火 (SA) 优化和支持向量机 (SVM) 分类算法相结合，用于在多个站点进行标准化降水指数 (SPI) 建模。提出的模型可以通过智能使用附近站的SPI系列作为模型输入来预测数据缺乏的气象站不同时间尺度的SPI。通过土耳其安卡拉省 Kecioren 站的交叉验证技术，检验了混合模型对气象干旱多站预测的能力。为此，车站的 SPI-3、SPI-6 和 SPI-12 使用附近五个车站的相同指数进行建模。在第一步中，SVM 使用不同的内核进行训练，以生成和分类一组合理的多站预测场景。然后，在每个场景下使用ENN对SPI系列进行回归，最后利用集成模型的SA组件来提高ENN效率。使用各种错误和复杂性措施来检测模型的性能。结果表明，ENN-SA 对于多站 SPI 预测是有前途和有效的。使用各种错误和复杂性措施来检测模型的性能。结果表明，ENN-SA 对于多站 SPI 预测是有前途和有效的。使用各种错误和复杂性措施来检测模型的性能。结果表明，ENN-SA 对于多站 SPI 预测是有前途和有效的。