Model Tangki-Codeq untuk Transformasi Seri Data Hujan menjadi Aliran Sungai Periode Harian
DOI:
https://doi.org/10.22219/jmts.v22i1.35939Abstract
Algoritma Codeq merupakan sintesa dari chaotic search, opposition-based learning, diferential evolution dan quantum mechanism. Keandalannya dalam menyelesaikan sistim persamaan non linier dan komplek menjadikan metode ini menarik diterapkan untuk menyelesaikan berbagai masalah optimasi. Penelitian ini mengkaji efektivitas model Tangki-Codeq pada analisis transformasi seri data hujan menjadi seri data aliran periode harian. Model Tangki-Codeq merupakan model dari hasil penggabungan sistim persamaan simulasi model Tangki Sugawara dan metode optimasi parameter berbasis algoritma Codeq. Pengujian model dilakukan di daerah aliran Sungai (DAS) Welang Jawa Timur dengan melibatkan set data amatan periode harian sepanjang 15 tahun, yaitu Tahun 2016 – 2020. Hasil pengujian menunjukkan model Tangki-Codeq mampu mempresentasikan hubungan seri data hujan menjadi data debit Sungai Welang periode harian dengan sangat efektif. Indikator Nash-Sutcliffe Efficiency (NSE) > 0.8 yang dihasilkan dari tahap kalibrasi dan validasi menunjukkan kurva debit luaran model dapat mendekati kurva debit amatan.
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