Bour Saeid, Kayhomayoon Zahra, Hassani Farhad, Ghordoyee Milan Sami, Bazrafshan Ommolbanin, Berndtsson Ronny
Department of Civil Engineering, Islamic Azad University Nour Branch, Nur, Iran.
Department of Geology, Payame Noor University, Tehran, Iran.
PLoS One. 2025 Mar 18;20(3):e0319678. doi: 10.1371/journal.pone.0319678. eCollection 2025.
Drought is a climate risk that affects access to safe water, crop development, ecological stability, and food production. Therefore, developing drought prediction methods can lead to better management of surface and groundwater resources. Similarly, machine learning can be used to find improved relationships between nonlinear variables in complex systems. Initially, the standardized precipitation evapotranspiration index (SPEI) was calculated, and then using large-scale signals such as large-scale climate signals (the North Atlantic Oscillation, the Arctic Oscillation, the Pacific Decadal Oscillation, and the Southern Oscillation Index), along with climatic variables including temperature, precipitation, and potential evapotranspiration, predictions were made for the period of 1966-2014. Several new machine learning models including Least Square Support Vector Regression (LSSVR), Group Method of Data Handling (GMDH), and Multivariate Adaptive Regression Splines (MARS) were used for prediction. The results showed that in estimating SPEI in moderately arid climates, the GMDH model with criteria (RMSE = 0.26, MAE = 0.17, NSE = 0.95 in validation) under scenario S1 (included all variables plus the SPEI of the previous month) performed better, while in arid and cold climates, the LSSVR model (RMSE = 0.22, MAE = 0.18, NSE = 0.95 in validation) under S1, and in arid and hot climate, the LSSVR model (RMSE = 0.29, MAE = 0.19, NSE = 0.93 in validation) under scenario S2 (included meteorological variables plus the SPEI of the previous month) had higher prediction accuracy. Although the MARS model was less accurate in validation, it showed higher accuracy during calibration compared to the other two models in all climates. The results showed that using large-scale signals for predicting SPEI was beneficial. It can be concluded that machine learning models are useful tools for predicting the SPEI drought index in different climates within similar ranges.
干旱是一种气候风险，会影响安全用水的获取、作物生长、生态稳定性和粮食生产。因此，开发干旱预测方法有助于更好地管理地表水和地下水资源。同样，机器学习可用于在复杂系统中寻找非线性变量之间的改进关系。首先计算标准化降水蒸散指数（SPEI），然后利用诸如大规模气候信号（北大西洋涛动、北极涛动、太平洋年代际振荡和南方涛动指数）等大规模信号，以及包括温度、降水和潜在蒸散在内的气候变量，对1966 - 2014年期间进行预测。使用了几种新的机器学习模型，包括最小二乘支持向量回归（LSSVR）、数据处理分组方法（GMDH）和多元自适应回归样条（MARS）进行预测。结果表明，在中等干旱气候下估计SPEI时，情景S1（包括所有变量加上前一个月的SPEI）下标准为（验证时RMSE = 0.26，MAE = 0.17，NSE = 0.95）的GMDH模型表现更好，而在干旱寒冷气候下，情景S1下的LSSVR模型（验证时RMSE = 0.22，MAE = 0.18，NSE = 0.95），以及在干旱炎热气候下，情景S2（包括气象变量加上前一个月的SPEI）下的LSSVR模型（验证时RMSE = 0.29，MAE = 0.19，NSE = 0.93）具有更高的预测精度。尽管MARS模型在验证时精度较低，但在所有气候条件下，与其他两个模型相比，它在校准期间显示出更高的精度。结果表明，使用大规模信号预测SPEI是有益的。可以得出结论，机器学习模型是在相似范围内预测不同气候下SPEI干旱指数的有用工具。
