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All Journal Jurnal Produksi Tanaman JTSL (Jurnal Tanah dan Sumberdaya Lahan) Journal of Degraded and Mining Lands Management Jurnal Wilayah dan Lingkungan Jurnal Hutan dan Masyarakat JEPA (Jurnal Ekonomi Pertanian dan Agribisnis) Bina Hukum Lingkungan Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat AbdiTechno (Jurnal Pengabdian Masyarakat) Devotion: Journal Corner of Community Service Bina Hukum Lingkungan INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) RADIKULA: Jurnal Ilmu Pertanian Abdibaraya: Jurnal Pengabdian Masyarakat Bioeksperimen
Dhina Mustikaningrum
Universitas Sunan Bonang
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FLOOD SUSCEPTIBILITY MAPPING USING MACHINE LEARNING IN KENING RIVER, SUB WATERSHED OF BENGAWAN SOLO, TUBAN Dhina Mustikaningrum; Liadira Kusuma Widya; Umayya Ulfah; Regita Faridatunisa Wijayanti
INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY VOLUME 7, NUMBER 2, OCTOBER 2024
Publisher : Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/urbanenvirotech.v7i2.18818

Abstract

Floods are natural occurrences with the potential to cause damage to ecosystems and pose significant threats to human life, resulting in the destruction of property, infrastructure, and socioeconomic challenges. In recent times, flooding in the Sub-Watershed of Bengawan Solo has been linked to the overflowing Kening River in Tuban County. Aim: This study aims to produce a flood susceptibility map to mitigate the frequency of flood occurrences as well as facilitate effective planning for flood disaster risk management. Methodology and results: Flood data is collected from 2016 to 2023 through field surveys, Sentinel-1 satellite imagery, and data from the Development Planning Agency, Tuban County. Integrating remote sensing data from satellite imagery (PlanetScope, Sentinel-2), geographic information systems (GIS), and spatial modeling techniques, a flood susceptibility map is developed for the Kening River catchment. The occurrence of floods in the Kening River area is associated with various factors (11 variables) assessed through the frequency ratio approach, including profil curvature, LS factor, aspect, rainfall, river distance, road distance, building density 100 m, road density 100 m, vegetation type, normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI). The results show flood susceptibility maps utilizing frequency ratio (FR) and convolutional neural network (CNN) techniques. The flood susceptibility map obtained through the CNN method demonstrates a notably high AUC value. The model development generated a validation AUC value of 0.857 for training and 0.856 for testing. Conclusion, significance and impact study: This research provides an understanding into the factors that influence the occurrence of floods in the Kening River catchment area. It also emphasizes the benefit of advanced machine learning approaches in mapping the susceptibility of floods. Furthermore, this study has the potential to be helpful in guiding regional policy decisions and result in enhanced flood risk management measures in Tuban County.
Co-Authors Abdi Dewi Setyana Alif Ayu Umiyana Anisa Rosida Didik Suprayogo Eka Lestari, Sulistyani Herry Prasetyo Hery Prasetyo, Hery Khairiyah, Yaumil Kristiawan KRISTIAWAN KRISTIAWAN Kristiawan Kristiawan Liadira Kusuma Widya Lucky Febriansyah M. Yogi Riyantama Isjoni Maimunah Maimunah Maimunah Maimunah, Maimunah Maja Pranata Marbun Marbun, Maja Pranata Maulidi Firlandiana Muhammad Zaki Arbiansyach Nia Nurfitria Perala, Iwan Prasetya, Herry Prasetyo S, Hery Regita Faridatunisa Wijayanti RM. Armaya Mangkunegara Setiyanta, Nunuk Candra Sholihah, Puri Kholifatush Sri Rahayu Utami Sriwulan Sriwulan Sugiyanto - Sulistyani Eka Lestari Suprayitno Suprayitno Suprayitno Suprayitno Susanto, Harry Umayya Ulfah Widodo, Teguh Endi