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All Journal JOIV : International Journal on Informatics Visualization Bulletin of Geology
Fajary, Faiz Rohman
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Computer Science & IT Earth & Planetary Sciences Energy Engineering

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KAJIAN KETIDAKPASTIAN ESTIMASI CURAH HUJAN SATELIT TRMM STUDI KASUS: DAS CILIWUNG Syahputra, Muhammad Ridho; Fajary, Faiz Rohman; Riawan, Edi; Suwarman, Rusmawan; Putra, Hengki Eko
Bulletin of Geology Vol 6 No 3 (2023): Bulletin of Geology
Publisher : Fakultas Ilmu dan Teknologi Kebumian (FITB), Institut Teknologi Bandung (ITB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/bull.geol.2022.6.3.4

Abstract

Dataset curah hujan (CH) dengan resolusi spasial dan temporal yang baik diperlukan oleh berbagai sector seperti sektor manajemen sumber daya air dan bencana hidrologi. Namun, pengamatan CH secara langsung di permukaan seringkali tidak lengkap dan tidak merata. Sumber alternatif data CH lainnya dapat diperoleh dari estimasi CH oleh satelit (penginderaan jauh). Namun, estimasi satelit memiliki kesalahan dan ketidakpastian yang berpotensi mempengaruhi aplikasi selanjutnya yang menggunakan data hujan. Oleh karena itu, penting untuk mengukurketidakpastian estimasi CH satelit untuk memberikan informasi tambahan bagi pengguna mengenai keandalan data. Pada penelitian ini telah dilakukan simulasi Monte Carlo untuk menghasilkan ensemble Dataset CH yang mampu mengkuantifikasi ketidakpastian produk estimasi CH TRMM (Tropical Rainfall Measuring Mission) 3B43v7. Dataset CH ensemble ini dapat digunakan untuk memberikan informasi tentang distribusi error dan estimasi peluang CH dari estimasi satelit TRMM. Dalam kajian ini juga ditunjukkan pemanfaatan dataset CH ensemble dalam mengkuantifikasi ketidakpastian dalam analisis CH ekstrim menggunakan pendekatan periode ulang dan Probable Maximum Precipitation (PMP). Kata kunci: estimasi satelit, TRMM, ketidakpastian, Monte Carlo, ensemble
Performance Evaluation of a Simple Feed-forward Deep Neural Network Model Applied to Annual Rainfall Anomaly Index (RAI) Over Indramayu, Indonesia Herho, Sandy Hardian Susanto; Irawan, Dasapta Erwin; Fajary, Faiz Rohman; Suwarman, Rusmawan; Kaban, Siti Nurzannah
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.1984

Abstract

Indramayu is a district in West Java that is known for being the leading producer of rice and brackish salt. The production of these two commodities is strongly influenced by hydroclimatological conditions, making accurate and reliable long-term estimates crucial. In this study, we evaluated a simple feed-forward deep neural network (DNN) model that could potentially be used as a candidate for statistical guidance to improve the accuracy of a mesoscale numerical climate model. We used the spatial average of the accumulated annual rainfall of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data as an input time series with a time range from 1981 to 2022. This data was then processed into annual rainfall anomaly index (RAI) data. The Annual RAI was divided into training and test sets, and the feed-forward DNN model was fitted to the annual RAI in the training set. The accuracy of the model was then tested in the test set using the root-mean-square error (RMSE) metric. Our study shows that the feed-forward DNN model is unsuitable for estimating the annual RAI over Indramayu. The RMSE values are significantly high in the training and test sets.
Co-Authors Dasapta Erwin Irawan, Dasapta Erwin Edi Riawan, Edi Herho, Sandy Hardian Susanto Kaban, Siti Nurzannah Putra, Hengki Eko Suwarman, Rusmawan Syahputra, Muhammad Ridho