Time-Series Model for Climatological Forest Fire Prediction over Borneo
Abstrak
Daerah yang ditutupi oleh hutan tropis, seperti Kalimantan, rentan terhadap kebakaran. Studi-studi sebelumnya telah menunjukkan bahwa data iklim merupakan salah satu faktor penting yang mempengaruhi kebakaran hutan. Penelitian ini bertujuan untuk memprediksi kebakaran hutan di Kalimantan dengan mempertimbangkan aspek temporal dari data iklim. Kami menggunakan model berbasis time-series, model Long Short-Term Memory (LSTM). Kami menerapkan tiga jenis model LSTM, yaitu model Basic, Bidirectional, dan Stacked. Kami melakukan tiga eksperimen berbeda dari Januari 1998 hingga Desember 2015 dengan menguji data iklim, Oceanic Nino Index (ONI) dan indeks Indian Ocean Dipole (IOD). Kami mengevaluasi model dengan Mean Absolute Error (MAE) dan angka korelasi. Hasilnya, semua model dapat menangkap pola spasial dan temporal kebakaran hutan untuk ketiga eksperimen, di mana prediksi terbaik terjadi pada bulan September dengan korelasi spasial minimal 0,75. Berdasarkan metrik evaluasi, Stacked LSTM dalam Eksperimen 1 sedikit lebih unggul dengan korelasi pola tahunan tertinggi (0,89) dan kesalahan terendah (0,71). Temuan ini menunjukkan bahwa penambahan indeks ONI dan IOD sebagai fitur prediksi tidak akan meningkatkan kinerja model secara umum, tetapi secara spesifik meningkatkan nilai pada kejadian ekstrem.
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