Abstract

Agricultural drought refers to soil moisture deficit, which causes adverse effects on the crop production and economy of a nation. This work compared the capability of artificial neural network (ANN) and support vector machine (SVM) algorithm in predicting agricultural drought in the Palakkad district of Kerala, India. Also, the influence of various global climatic indices on soil moisture stress in the study area is assessed. Two models were developed to investigate the impact of global climatic indices. Model 1 considered only local meteorological variables as predictors, and model 2 included global climatic indices along with meteorological variables. The results showed that ENSO has commendable influence on the early prediction of agricultural drought in Palakkad and are more evident at higher lead times (2 to 4 months). For the first model of ANN and SVM, the R² values at a 4-month lead range from 0.56 to 0.76 and 0.62 to 0.77, respectively. Similarly, for model 2, the R² varies from 0.61 to 0.77 and 0.75 to 0.82 for ANN and SVM models, respectively. Further, the results indicated that the SVM model shows clear advancement in prediction over ANN especially at higher lead times, even though both show a comparable performance at 1-month lead time. The study provided useful information regarding the potential predictors of agricultural drought in the study area and suggest suitable models for the early prediction. This will support the decision makers in drought prevention and water resource management.

中文翻译：

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使用全球气候指数对印度帕拉卡德地区基于机器学习的农业干旱进行预测

摘要

农业干旱是指土壤水分亏缺，对一个国家的农作物生产和经济造成不利影响。这项工作比较了人工神经网络（ANN）和支持向量机（SVM）算法在印度喀拉拉邦帕拉卡德地区预测农业干旱的能力。此外，还评估了各种全球气候指数对研究区土壤水分胁迫的影响。开发了两个模型来研究全球气候指数的影响。模型 1 仅考虑当地气象变量作为预测变量，模型 2 包括全球气候指数和气象变量。结果表明，ENSO 对帕拉卡德农业干旱的早期预测具有值得称赞的影响，并且在较长的提前期（2 至 4 个月）内更为明显。对于 ANN 和 SVM 的第一个模型，4 个月领先时的R ^{2值范围分别为 0.56 至 0.76 和 0.62 至 0.77。}同样，对于模型 2， ANN 和 SVM 模型的R ²分别在 0.61 到 0.77 和 0.75 到 0.82 之间变化。此外，结果表明，SVM 模型在预测方面比 ANN 表现出明显的进步，尤其是在较长的交付周期内，尽管两者在 1 个月的交付周期内表现出相当的性能。该研究提供了有关研究地区农业干旱潜在预测因素的有用信息，并为早期预测提出了合适的模型。这将为干旱预防和水资源管理的决策者提供支持。