Analisa Prediksi Curah Hujan dengan Metode Kalman Filter dan Mean Field Bias Menggunakan Data Satelit GSMaP dan Stasiun Observasi (Studi Kasus : Kota Surabaya Tahun 2023)

Annabiila, Elvara Ardhya Putri (2024) Analisa Prediksi Curah Hujan dengan Metode Kalman Filter dan Mean Field Bias Menggunakan Data Satelit GSMaP dan Stasiun Observasi (Studi Kasus : Kota Surabaya Tahun 2023). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Curah hujan merupakan data spasial yang berkaitan dengan berbagai fenomena alam mulai dari bencana alam, perubahan cuaca bahkan iklim. Data curah hujan di Indonesia didapatkan dari pengukuran tiap titik stasiun pengamatan Belum meratanya stasiun pengamatan menyebabkan proses prediksi curah hujan tidak optimal. Keterbatasan ini dapat diatasi dengan menggunakan teknologi penginderaan jauh salah satunya satelit cuaca. Salah satu satelit curah hujan global yang dapat digunakan ialah GSMaP atau Global Satellite Measurement of Precipitation. Resolusi sebesar 11,06 x 11,06 km milik satelit ini mampu mencakup semua area Indonesia. Namun demikian, perlu dilakukan integrasi antara data stasiun pengamatan curah hujan dan satelit GSMaP. Integrasi ini digunakan sebagai validasi guna mengetahui besarnya nilai bias antara kedua sistem. Koreksi dihitung menggunakan metode Mean Field Bias dimana parameter yang digunakan ialah data curah hujan hasil interpolasi IDW. Dihasilkan nilai curah prediksi Kota Surabaya tahun 2023 dengan intensitas tertinggi sebesar 542,5 mm pada bulan Februari sedangkan intensitas terendah yakni 0 pada bulan Agustus hingga Oktober. Hasil pemodelan dengan metode ini menghasilkan RMSE sebesar 0,9 serta nilai korelasi sebesar 1, sedangkan pada perhitungan Mean Error dan Relative Bias dihasilkan nilai -0,2 dan -0,2%.
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Rainfall is one of the important spatial data cause its relevance to various natural phenomena, such as natural disasters, weather changes, and even climate. Rainfall data in Indonesia is obtained from measurements at each observation station. The uneven distribution of observation stations hinders the optimal rainfall prediction process. This limitation can be addressed by using remote sensing technology such as weather satellites. One of the global rainfall satellites that can be used is GSMaP, or Global Satellite Measurement of Precipitation. With a resolution of 11.06 x 11.06 km, this satellite can cover all areas of Indonesia well. However, it is necessary to integrate the data from both the rainfall observation stations and the GsMaP satellite. This integration is used for validation to determine the extent of bias between these two systems. Corrections can be calculated using the Mean Field Bias method, where the parameter used is the data resulting from the interpolation of rainfall values through Inverse Distance Weighting (IDW) interpolation. The highest predicted rainfall value in Surabaya for that year is 542,5 mm in February and the lowest is 0 mm for August to October. This model has an error value of 0.9 and correlation of 1, while the calculation of mean error and relative bias were -0.2 and -0.2%.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Curah Hujan, IDW, Mean Field Bias, Kalman Filter IDW, Kalman Filter, Mean Field Bias, Rainfall
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) > G70.217 Geospatial data G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions:	Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis
Depositing User:	ELVARA ARDHYA PUTRI ANNABIILA
Date Deposited:	29 Jul 2024 12:47
Last Modified:	29 Jul 2024 12:47
URI:	http://repository.its.ac.id/id/eprint/109358

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