Machine learning-based prediction of agricultural drought using global climatic indices for the Palakkad district in India

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.