CD Skripsi

Penggunaan Model Regresi Untuk Prediksi Muka Air Tanah Gambut (Studi Kasus : Ogan Komering Ilir)

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ABSTRACT
According to the peatland fire risk index, peat soils with a groundwater level of more than 40 cm are very at risk of land fires. Groundwater Surface (GS) plays an important role as a key in determining the risk of fire. So that if changes in groundwater levels in the future can be predicted, then the risk of fire can also be minimized. The purpose of this study is to create a hydrological model to predict the depth of the Groundwater Surface (GS) as a parameter for potential fires on peatlands using regression analysis, as an early warning of the risk of peatland fires. The data used for modeling are rainfall data and groundwater depth data that have been recorded historically on peatlands by installing the SESAME tool at the research location and obtained data through the website address SESAME-system.com. Regression analysis was performed using four data length scenarios, namely 3 months, 6 months, 9 months, and 12 months to determine the period that could represent the hydrological conditions in the field. The results showed that the regression analysis with data lengths of 6 months, 9 months, and 12 months gave the best predictive results for groundwater levels with high correlation coefficient values of 0,761; 0,813, and 0,968. Meanwhile, the prediction of the groundwater level using regression analysis with a data length of 3 months gives results with a high enough correlation value of 0,624.

Key words: Regression models, groundwater level, peatland, peatlands fire, correlation value

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

07 01. 120 (0104)

Penerbit

Pekanbaru : Universitas Riau – Fakultas Teknik – Teknik Sipil., 2020

Deskripsi Fisik

ix, 79 hlm.; ill.: 29 cm

Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

07 01. 120 (0104)

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNIK SIPIL

Info Detail Spesifik

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Pernyataan Tanggungjawab

Daus

Versi lain/terkait

Lampiran Berkas

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