Land Suitability Evaluation of Soybean as Drought Adaptation in Grobogan Regency

Partini, Dwi

doi:10.20961/ge.v6i1.36215

Cited by 1 publication

(1 citation statement)

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“…Therefore, rain predictions for Indramayu district are very important in order to maintain rice production in the area. Indonesia is chosen because it bears considerable losses due to hydro meteorological hazards such as floods [15] and drought ( [16]; [17]; [18]). Therefore, weather prediction at the S2S scale which has a relatively long daily prediction scale is needed to anticipate the impact of hydro meteorological disasters.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Voting Feature Interval using Empiric Distribution for Prediction of Rainfall Categories on Sub-Seasonal to Seasonal Scale

Kustiyo,

Buono,

Faqih

et al. 2024

JISR-C

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Predictions of rainfall categories on the sub-seasonal to seasonal (S2S) scale based on the Global Climate Model (GCM) output from the European Center for Medium-Range Weather Forecasts (ECMWF) have a prediction time range of up to 46 days ahead. The S2S predictions for the 46 days ahead need 46 models with one model per day. One of the methods for S2S rainfall prediction is the convolutional neural network (CNN) method. However, the CNN requires extensive computational resources. By contrast, the Voting feature interval (VFI) classification algorithm requires few resources, because the VFI training is performed once using all training data. However, the VFI algorithm has low prediction accuracy for classification problems with continuous features such as GCM data. This is due to determining the number of feature intervals based on the number of classes in the VFI training. Furthermore, the vote for each feature interval in the VFI training depends on the number of data in each interval only. This study proposes a modification of the standard VFI algorithm namely the Kernel VFI (KVFI). In KVFI, the vote for each feature interval is determined by the distribution of all data from that feature using the Kernel method. In addition, the proposed method allows for a more flexible determination of the number of feature intervals based on experiments. The newly proposed KVFI algorithm is then utilized to predict the rainfall categories in the Indramayu Regency, Indonesia. The prediction results show that the KVFI algorithm produces higher accuracy than the standard VFI.

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Section: Introductionmentioning

confidence: 99%