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Model Sistem Prediksi Ensemble Total Hujan Bulanan Dengan Nilai Pembobot [PAPER]

ABSTRAK Manajemen air menjadi sangat penting khususnya di wilayah yang rentan terhadap ketersediaan air. Mengingat hujan di atas nor...





ABSTRAK

Manajemen air menjadi sangat penting khususnya di wilayah yang rentan terhadap ketersediaan air. Mengingat hujan di atas normal dapat mengakibatkan banjir sedangkan hujan di bawah normal mengakibatkan kekeringan. Untuk itu prediksi unsur iklim hujan ini menjadi penting. Model sistem prediksi ensemble berbasis model sistem prediksi tunggal ANFIS, lavele/-ANFIS, Wavelet ARIMA, dan ARIMA total hujan bulanan telah disimulasikan di wilayah Kabupaten Indramayu. Model sistem prediksi ensemble total hujan bulanan ini dibentuk dengan teknik pembobotan. Nilai pembobot didasarkan pada nilai koefislen korelasi Pearson (r) yang diperoleh selama masa pelatihan dengan series data 1991-2000. Hasil pengolahan data 2001-2009 menunjukkan kisaran nilai r didapat 0,45-0,83 untuk ANFIS; 0,20-0,53 untuk Wavelet-ANFIS 50-0,95 untuk Wavelet-ARIMA; 0,14-0,66 untuk ARlMA; dan 0,58-0,94 untuk Ensemble. Secara spasial, luaran model astem prediksi ensemble total hujan bulanan di wilayah Kabupaten Indramayu menunjukkan hasil yang konsisten lebih baik daripada luaran model sistem prediksi tunggal pembentuknya.

Kata Kunci: Ensamble, Hujan, Koefien korelasi, Model, Pembobotan


ABSTRACT

Water management is very important especially in areas that are vulnerable to water availability. Given the above normal rain can cause flooding while below normal rain can cause drought. For this reason the prediction of elements of rain climate is important. The ensemble prediction system model based on the single prediction system model ANFIS, lavele / -ANFIS, Wavelet ARIMA, and ARIMA total monthly rainfall has been simulated in the Indramayu Regency. This monthly ensemble prediction system model is formed by weighting techniques. The weighting value is based on the Pearson correlation coefficient (r) obtained during the training period with the 1991-2000 data series. The results of 2001-2009 data processing showed that the range of r values ​​was 0.45-0.83 for ANFIS; 0.20-0.53 for Wavelet-ANFIS 50-0.95 for Wavelet-ARIMA; 0.14-0.66 for ARlMA; and 0.58-0.94 for the Ensemble. Spatially, the output of the astem prediction model ensemble the total monthly rainfall in the Indramayu Regency region shows that the results are consistently better than the outputs of the single prediction system models that form it.


Keywords: Ensemble, Rain, Correlation Coefficient, Model, Weighting


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RSGIS Indonesia: Model Sistem Prediksi Ensemble Total Hujan Bulanan Dengan Nilai Pembobot [PAPER]
Model Sistem Prediksi Ensemble Total Hujan Bulanan Dengan Nilai Pembobot [PAPER]
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