Pemodelan Banjir Genangan Di Kota Surabaya Berdasarkan Data Bmkg Dengan Menggunakan Regresi Inverse Gaussian Dan Regresi Gamma

Mahendra, Angga (2019) Pemodelan Banjir Genangan Di Kota Surabaya Berdasarkan Data Bmkg Dengan Menggunakan Regresi Inverse Gaussian Dan Regresi Gamma. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Secara Geografis Indonesia terletak diantara dua benua (Benua Asia dan Benua Australia) dan dua samudra (Pasifik dan Hindia). Indonesia dilalui oleh angin monsun yaitu angin monsun Barat dan angin monsun Timur. Dampak negatif yang ditimbulkan akibat intensitas curah hujan yang tinggi di Surabaya adalah banjir. Penelitian ini akan dilakukan pemodelan curah hujan menggunakan data curah hujan harian mulai tahun 2010 sampai dengan 2018 di 6 stasiun Kota Surabaya yaitu Gunungsari, Wonokromo, Wonorejo, Keputih, Kedung Cowek, dan Gubeng. Prediksi curah hujan dilakukan dengan pendekatan regresi Inverse Gaussian dan regresi Gamma. Variabel prediktor yang digunakan merupakan variabel yang memengaruhi curah hujan seperti kelembaban rata rata, kecepatan angin rata rata, dan suhu udara (temperature) rata rata. Pemodelan dilakukan secara univariat dengan multivariate. Pemodelan curah hujan univariate menghasilkan pemodelan yang terbaik yaitu berupa pasangan reespon-prediktor CH Keputih – Juanda, CH Kd. Cowek – Perak2, CH. Gubeng- Juanda, CH. Wonorejo – Juanda, CH. Wonokromo – Juanda, dan CH – Gn. Sari- Juanda. Kesimpulan yang didapatkan bahwa hasil prediksi curah hujan maksimum sangat rendah (tertinggi pada balai hujan Gubeng sebesar 4.121) sehingga tidak ada saluran yang berpotensi banjir di kota Surabaya.
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Indonesia is located between two continents (Continental Asia and Continental Australia) and two oceans (Pacific and Indian). Indonesia is traversed by the monsoon namely the West monsoon and the East monsoon. The negative impact caused by the intensity of high rainfall in Surabaya is flooding. Rainfall modeling in this work is conducted using daily rainfall data from 2010 to 2018 in six stations, namely Gunungsari, Wonokromo, Wonorejo, Keputih, Kedung Cowek, and Gubeng. The rainfall prediction is done using Inverse Gaussian regression and Gamma regression approaches. The predictor used is variable that affects rainfall such as average humidity, average wind speed, and average air temperature. The modeling is done by multivariate and univariate in term of number of prediktor. Univariate rainfall modeling produces the best modeling with pair of rainfall-prediktor’s station as CH Keputih - Juanda, CH Kd. Cowek - Perak2, CH. Gubeng-Juanda, CH. Wonorejo - Juanda, CH. Wonokromo - Juanda, and CH Gunung Sari - Juanda. The result concludes that the maximum rainfall prediction are very low (the highest in the Gubeng rainforest hall is 4,121 mm) so there are no potentially flooded channels in the city of Surabaya.

Item Type:	Thesis (Other)
Additional Information:	RSSt 519.536 Mah p-1 2019
Uncontrolled Keywords:	Inverse Gaussian, Regresi Gamma, Fungsi Link, Curah Hujan
Subjects:	G Geography. Anthropology. Recreation > GB Physical geography > GB1399.9 Floods Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Divisions:	Faculty of Mathematics, Computation, and Data Science > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Angga Mahendra
Date Deposited:	23 May 2023 04:51
Last Modified:	23 May 2023 04:51
URI:	http://repository.its.ac.id/id/eprint/64501

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