Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods

Kuswanto, Heri; Naufal, Achmad

doi:10.1016/j.mex.2019.05.029

Cited by 24 publications

(10 citation statements)

References 36 publications

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“…A more recent study, Rahmati et al (2020) compared the performance of six different ML techniques [classification and regression trees (CART), boosted regression trees (BRT), random forests, multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA), and SVM] for mapping agricultural drought hazard in the southeast region of Queensland, Australia. Similar to Park et al (2016) and Kuswanto and Naufal (2019), they found that random forests had the best goodness-of-fit and predictive performance among the six models. Zaniolo et al (2018) contributed to the FRIDA (FRamework for Index-based Drought Analysis) for the automatic design of basin-customized drought indexes across different types of basins by applying a MLpowered variable selection algorithm.…”

Section: Advancement In the Use Of Machine Learning Techniques For Drought Predictionmentioning

confidence: 67%

“…Their findings suggest that among the three approaches, random forests provide the best performance for Standardized Precipitation Index (SPI) prediction. Similarly, Kuswanto and Naufal (2019) found the performance of random forests to be optimal when using SPI derived from Modern-Era Retrospective analysis for Research and Applications (MERRA-2) for drought prediction over the East Nusa Tenggara Province in Indonesia. A more recent study, Rahmati et al (2020) compared the performance of six different ML techniques [classification and regression trees (CART), boosted regression trees (BRT), random forests, multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA), and SVM] for mapping agricultural drought hazard in the southeast region of Queensland, Australia.…”

Section: Advancement In the Use Of Machine Learning Techniques For Drought Predictionmentioning

confidence: 96%

“…During the last decade, there has been an increase in the use of ML techniques to improve drought predictability (Hao et al, 2018). For instance, random forest ML algorithms have been increasingly used in drought prediction studies (Park et al, 2016;Kuswanto and Naufal, 2019;Rahmati et al, 2020). Random forests are extensions of decision tree analysis that start with classification trees-types of decision trees that can be grown together as a "forest" in a computational system.…”

Section: Advancement In the Use Of Machine Learning Techniques For Drought Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

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Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.

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Section: Advancement In the Use Of Machine Learning Techniques For Drought Predictionmentioning

confidence: 67%

Section: Advancement In the Use Of Machine Learning Techniques For Drought Predictionmentioning

confidence: 96%

Section: Advancement In the Use Of Machine Learning Techniques For Drought Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

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“…From the point of view of the households characteristics, further analysis using Random Forest method of Breiman (2001) can be used to investigate the importance factors influencing the decision to use the forecast. Random Forest has been proven to be a powerful machine learning method compared to others (Kuswanto and Naufal, 2019). The method confirmed that that the usage of the forecast is mainly influenced by the age of the household head.…”

Section: Resultsmentioning

confidence: 99%

Perception of weather and seasonal drought forecasts and its impact on livelihood in East Nusa Tenggara, Indonesia

Kuswanto

Hibatullah

Soedjono

2019

Heliyon

Self Cite

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A cross-sectional study was conducted in 2018 to assess the perception of households on drought forecasts and its impact on crop and livestock losses. A total of 300 households from seven districts in East Nusa Tenggara Indonesia were considered. The study indicated that the majority of the households are poor families with low education background. They sold poultry for income generation during drought events. The survey revealed that only small percentage of the households usied forecast to support their livelihood management. The statistical test confirmed that the use of forecast did not necessarily impacted the crop loss. However, the crops were significantly affected by the response to drought forecast. Households that changed their agricultural practice experienced significantly different losses than households that did not do anything differently to their crops. The households argued that the accuracy of the forecasts issued by the government was very low. Therefore, it is recommended that policymakers and government authorities provide more accurate forecasts and a better strategy to increase household awareness of using drought forecasts.

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“…Metode-metode yang secara umum digunakan untuk menangani kondisi seperti itu adalah metode dari pendekatan level data. seperti yang dilakukan oleh [3] yang menggunakan SMOTE sebagai metode untuk menangani ketidakseimbangan kelas pada dataset yang digunakan. Terdapat metode lain yaitu ADASYN yang merupakan pemutakhiran dari metode SMOTE.…”

Section: Pendahuluanunclassified

Seleksi Fitur dan Penanganan Imbalanced Data menggunakan RFECV dan ADASYN

Pratama¹,

Chandra²,

Presetyaningrum³

2022

eksplora

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Proses data mining bekerja terhadap data yang tersedia. Jika dataset tidak tersedia sepenuhnya, hasil pengolahan data mining menjadi tidak optimal. Terdapat beberapa kondisi data yang perlu penanganan terlebih dahulu sebelum memasuki tahap data mining. Salah satunya ialah imbalanced class yang merupakan kondisi di mana distribusi data pada setiap kelas tidak proporsional. Sebagai salah satu cara untuk efisiensi proses klasifikasi, seleksi fitur dapat memenuhi kebutuhan tersebut karena hasil dari seleksi fitur adalah sebuah dataset dengan jumlah atribut yang lebih sedikit dari sebelumnya. Untuk menyelesaikan permasalahan imbalanced class, ADASYN digunakan dalam penelitian ini sebagai metode untuk menyeimbangkan proporsi kelas pada dataset. Sedangkan RFECV digunakan sebagai metode fitur seleksi yang dapat meningkatkan efisiensi pada proses klasifikasi. Setelah dilakukan evaluasi dari hasil klasifikasi pada dataset yang menggunakan seleksi fitur, didapatkan hasil klasifikasi yang lebih baik dibandingkan dengan hasil klasifikasi pada dataset tanpa seleksi fitur. Hal tersebut dibuktikan dengan perbandingan antara hasil terbaik dari akurasi klasifikasi dataset tanpa seleksi fitur. Hasil dari metode CART sebesar 85.1% yang merupakan hasil dari pengolahan data tanpa menggunakan metode fitur seleksi. sedangkan metode Bagging k-NN yang menghasilkan akurasi sebesar 88% yang di aplikasikan pada dataset dengan seleksi fitur. Sehingga dapat disimpulkan bahwa seleksi fitur dapat meningkatkan akurasi pada klasifikasi.

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Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods

Abstract: Graphical abstract

Cited by 24 publications

References 36 publications

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Perception of weather and seasonal drought forecasts and its impact on livelihood in East Nusa Tenggara, Indonesia

Seleksi Fitur dan Penanganan Imbalanced Data menggunakan RFECV dan ADASYN

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