Koreksi Bias Data Hujan Luaran GCM ECHAM5 Untuk Prediksi Curah Hujan Bulanan dan Musiman Pulau Lombok

Saidah, Humairo; Setiawan, Agustono; Hanifah, Lilik; Pradjoko, Eko; Suroso, Agus

doi:10.29303/jstl.v7i2.289

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…signi cantly in uences forecasting results and stands as a critical factor in statistical downscaling approach [21]. Global GCM data reduction is carried out by cropping the domain through climatological reduction.…”

Section: Datamentioning

confidence: 99%

Optimizing Machine Learning for Daily Rainfall Prediction in Bogor City, Indonesia: A Two-Stage Statistical Downscaling Approach

Arassah,

Sadik,

Sartono

et al. 2024

Preprint

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Statistics for machine learning come as a significant tool for studying data. General Circulation Model (GCM) are the most sophisticated model for predicting climate and weather. This study deployed a two stage of machine learning model for statistical downscaling approach to predict daily rainfall in Bogor, Indonesia. This study compared three different domains of GCM and compare two different approaches to handling missing data. First, we made two datasets based on approaches to handling missing value. Then, Support Vector Classification model was applied to classify rainy and non-rainy days. Finally, we developed a model of rainy-day data using Recurrent Neural Networks (RNN) method to estimate daily rainfall. The results show that using random forest imputation for handling missing value can increase the accuracy and lower the RMSE of the model. The best domain from GCM data is 5 km from local station climatology. SVC model with radial basis kernel is the best model for classify rainy and non-rainy data with 0.985 (98.5%) accuracy and RNN model have RMSE at 16.19. Accurately estimating the increase or decrease in extreme rainfall is crucial to provide effective recommendations in disaster mitigation efforts.

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Section: Datamentioning

confidence: 99%

Optimizing Machine Learning for Daily Rainfall Prediction in Bogor City, Indonesia: A Two-Stage Statistical Downscaling Approach

Arassah,

Sadik,

Sartono

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…However, observation of rainfall data in an area is challenging to obtain. This is a problem for several research fields, one of which is the analysis of water availability (Saidah et al, 2021). Due to damage to the rainfall gauge, the Climatological Station of Deli Serdang rainfall data may experience data gaps.…”

Section: Introductionmentioning

confidence: 99%

Validation of Rain Thickness Model Based on GPM Satellite and Surface Measurements

Lubis

Anggraini

Sirait

2022

TGEO

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The limited acquisition of rainfall data from the Climatological Station of Deli Serdang has resulted in constraints on research in several fields. Global Precipitation Measurement (GPM) has spatial and temporal advantages as a solution to this problem. This study aims to provide information on the rain thickness equation model based on the Global Precipitation Measurement (GPM) Satellite as an alternative to calculating rain thickness at the Climatological Station of Deli Serdang. Data from the Global Precipitation Measurement (GPM) Satellite and Climatological Station of Deli Serdang were used, covering the period from January 1 to December 31, 2021. The Rescaled Adjusted Partial Sums (RAPS) method was used to verify the data quality, and the two data were consistent. The rain thickness equation model was obtained using a simple linear regression method. At the same time, the validation of the rain thickness equation model with surface measurements used the Root Mean Square Error (RMSE), Nash Sutcliffe Efficiency (NSE), and Correlation Coefficient (R) methods. The validation findings demonstrate that the rain thickness data from the Global Precipitation Measurement (GPM) Satellite was close to that of the Climatological Station of Deli Serdang, with an RMSE value of 5.85 mm, NSE of 0.99 mm, and R of 0.99. Hi = 5.21ti0.50 mm was the model used to calculate the equation for rain thickness. 6.84 mm, 0.69 mm, and 0.94 mm were the validation results of the equation model for RMSE, NSE, and R.Keywords: Rain Thickness, Global Precipitation Measurement (GPM), Climatological Station of Deli Serdang, Equation Model, Validation

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Koreksi Bias Data Hujan Luaran GCM ECHAM5 Untuk Prediksi Curah Hujan Bulanan dan Musiman Pulau Lombok

Cited by 2 publications

References 6 publications

Optimizing Machine Learning for Daily Rainfall Prediction in Bogor City, Indonesia: A Two-Stage Statistical Downscaling Approach

Optimizing Machine Learning for Daily Rainfall Prediction in Bogor City, Indonesia: A Two-Stage Statistical Downscaling Approach

Validation of Rain Thickness Model Based on GPM Satellite and Surface Measurements

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