ABSTRACT

Hydrological drought forecasting is a key component in water resources modeling as it relates directly to water availability. It is crucial in managing and operating dams, which are constructed in rivers. In this study, multiple extreme learning machines (ELMs) are utilized to forecast hydrological drought. For this purpose, the standardized hydrological drought index (SHDI) and standardized precipitation index (SPI) are computed for 1 and 3 aggregated months. Two scenarios are considered, namely, using SHDI in previous months as the input, and using SHDI and SPI in previous months as the input. Considering these scenarios and two timescales (1 and 3 months), 12 input–output combinations are generated. Then, five different ELMs and support vector machine models are used to predict the SHDI on both timescales. For preprocessing of the data, the wavelet is hybridized with the models, leading to 144 different models. The results indicate that ELMs are capable of forecasting SHDI with high precision. The self-adaptive differential evolution ELM outperforms the other models and the wavelet has a highly positive effect on the model performance, especially in error reduction. In general, using ELMs in hydrological drought forecasting is promising and this model can feasibly be used for this purpose.

摘要

水文干旱预报是水资源建模的关键组成部分，因为它直接关系到水资源的可用性。这对于管理和运营建在河流中的水坝至关重要。在这项研究中，使用多个极端学习机（ELM）来预测水文干旱。为此，标准化水文干旱指数 (SHDI) 和标准化降水指数 (SPI) 计算了 1 个月和 3 个月的合计月份。考虑两种情况，即以前几个月的 SHDI 作为输入，以前几个月的 SHDI 和 SPI 作为输入。考虑到这些情景和两个时间尺度（1 个月和 3 个月），生成了 12 种输入-输出组合。然后，使用五种不同的 ELM 和支持向量机模型来预测两个时间尺度上的 SHDI。对于数据的预处理，小波与模型混合，产生 144 个不同的模型。结果表明，ELMs 能够高精度地预测 SHDI。自适应差分进化ELM优于其他模型，小波对模型性能有非常积极的影响，特别是在减少误差方面。总的来说，在水文干旱预报中使用 ELM 是有希望的，并且该模型可以用于此目的。