Abstract
East Nusa Tenggara Province is one of the most vulnerable regions in Indonesia to drought. Drought prediction is definitely needed as a mitigation action to minimize the risk of drought. However, a sparse dataset has led to difficulties in accurately predicting future droughts in areas without meteorological stations, and hence a dataset with a finer resolution is required. This research investigates the performance of a 3-month Standardized Precipitation Index (SPI) derived from the Tropical Rainfall Measuring Mission (TRMM) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) to predict drought. CART and Random Forest are applied as the classification methods. Using several predictors, the analysis finds that CART has lower predictability than Random Forest. The average accuracy of the prediction using Random Forest reaches 100% with an average Area Under Curve (AUC) of about 0.8. The analysis also shows that predictions using the MERRA-2 dataset lead to higher accuracy and AUC than those using the TRMM. Therefore, using the MERRA-2 dataset predicted by Random Forest can be an optimal way to predict drought in East Nusa Tenggara. The methods confirmed that average soil surface temperature (day and night), Multivariate ENSO Index (MEI), Arctic Oscillation Index (AOI) and Normalized Difference Vegetation Index (NDVI) are strong predictors of drought. The performance of CART and Random Forest is improved with the Synthetic Minority Over-Sampling Technique (SMOTE).The techniques described:•translate drought information and predictors of drought into a base classifier that optimizes the AUC;•allow drought to be predicted for many grid points efficiently and with high accuracy; and•are computationally efficient and easy to implement.
Highlights
Method detailsDrought is a natural disaster of below-average precipitation in a certain area caused by disruption to an expected preciptation pattern, and it has a very wide impact
Using the data from July 2002 until August 2018, based on the 3-month Standardized Precipitation Index (SPI) data derived from Tropical Rainfall Measuring Mission (TRMM) and MERRA-2, it can be revealed that a drought happened almost every year in NTT, at either a moderate or a severe level
This is shown by the number of drought occurrences, where the TRMM data show a lower number of occurrences of moderate and severe droughts than MERRA-2
Summary
Drought is a natural disaster of below-average precipitation in a certain area caused by disruption to an expected preciptation pattern, and it has a very wide impact. Hatmoko et al [18] used TRMM data for drought analysis Another dataset that has been extensively used to build drought prediction is Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). The results showed that MERRA-2 provides valuable information consistent with observation, especially in the mid-latitudes, while uncertainties in the high latitudes are often large [27] This present paper investigates the performance of TRMM and MERRA-2 for predicting drought in East Nusa Tenggara, Indonesia. This research applies two different machine learning methods to classify the drought status, Classification and Regression Tree (CART) and Random Forest (RF) Both methods were selected because of their strength in applications to a large sample dataset, as in our case. This present paper proposes the combination of the machine learning methods with sampling method to overcome the problem of imbalance class response as well as to improve the predictive performance
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