Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods
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 meteo...
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ftdoajarticles:oai:doaj.org/article:71021e7601eb4ecb9be3be687fef88de 2023-05-15T15:12:03+02:00 Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods Heri Kuswanto Achmad Naufal 2019-01-01T00:00:00Z https://doi.org/10.1016/j.mex.2019.05.029 https://doaj.org/article/71021e7601eb4ecb9be3be687fef88de EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2215016119301499 https://doaj.org/toc/2215-0161 2215-0161 doi:10.1016/j.mex.2019.05.029 https://doaj.org/article/71021e7601eb4ecb9be3be687fef88de MethodsX, Vol 6, Iss , Pp 1238-1251 (2019) Science Q article 2019 ftdoajarticles https://doi.org/10.1016/j.mex.2019.05.029 2022-12-31T03:54:07Z 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. Method name: Random forest and CART, Keywords: Drought, Random forest, CART, Remote-sensing Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Merra ENVELOPE(12.615,12.615,65.816,65.816) MethodsX 6 1238 1251 |
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Science Q Heri Kuswanto Achmad Naufal Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods |
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Science Q |
description |
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. Method name: Random forest and CART, Keywords: Drought, Random forest, CART, Remote-sensing |
format |
Article in Journal/Newspaper |
author |
Heri Kuswanto Achmad Naufal |
author_facet |
Heri Kuswanto Achmad Naufal |
author_sort |
Heri Kuswanto |
title |
Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods |
title_short |
Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods |
title_full |
Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods |
title_fullStr |
Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods |
title_full_unstemmed |
Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods |
title_sort |
evaluation of performance of drought prediction in indonesia based on trmm and merra-2 using machine learning methods |
publisher |
Elsevier |
publishDate |
2019 |
url |
https://doi.org/10.1016/j.mex.2019.05.029 https://doaj.org/article/71021e7601eb4ecb9be3be687fef88de |
long_lat |
ENVELOPE(12.615,12.615,65.816,65.816) |
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Arctic Merra |
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Arctic Merra |
genre |
Arctic |
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Arctic |
op_source |
MethodsX, Vol 6, Iss , Pp 1238-1251 (2019) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2215016119301499 https://doaj.org/toc/2215-0161 2215-0161 doi:10.1016/j.mex.2019.05.029 https://doaj.org/article/71021e7601eb4ecb9be3be687fef88de |
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https://doi.org/10.1016/j.mex.2019.05.029 |
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MethodsX |
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