Analisis Risiko Longsor Di Wilayah Tropis Berdasarkan Curah Hujan Dan Topografi Berbasis DEM: Studi Literatur Sistematis
DOI:
https://doi.org/10.29103/tj.v16i1.1356Keywords:
Landslide, Rainfall, Topography, Digital Elevation Model, Systematic Literature ReviewAbstract
Abstrak
Risiko longsor di wilayah tropis dipicu interaksi iklim dan topografi, namun kajian sistematis yang memadukan keduanya dengan Digital Elevation Model (DEM) masih terbatas. Studi ini menelaah faktor pemicu dan pemanfaatan DEM dalam analisis kerentanan. Metodologi menggunakan analisis tematik terhadap 19 artikel (2015-2025) dari Google Scholar dan Scopus. Hasil menunjukkan lereng curam dan hujan intensitas tinggi sebagai pemicu dominan, terutama di kawasan bergunung. Secara keseluruhan, risiko longsor di wilayah tropis yang dikaji didominasi kategori kerawanan sedang hingga sangat tinggi. DEM terbukti efektif mengekstraksi parameter elevasi, kemiringan, dan aliran. Metode Frequency Ratio dan Analytic Hierarchy Process mencapai akurasi tertinggi (Area Under Curve/AUC 0,90-0,91), diikuti Random Forest (0,85). Kajian ini menyintesis temuan iklim-topografi tropis dan mengidentifikasi celah penelitian terkait integrasi data hujan temporal, resolusi DEM, serta validasi lapangan.
Kata kunci: Longsor, Curah Hujan, Topografi, Digital Elevation Model, Studi Literatur Sistematis
Abstrack
Landslide risk in tropical regions is driven by climatic and topographic interactions, yet systematic studies integrating both with Digital Elevation Model (DEM) data remain limited. This systematic review examines landslide triggers and DEM utilization in vulnerability analysis. The methodology involves a thematic analysis of 19 articles (2015-2025) from Google Scholar and Scopus. Results identify steep slopes and high-intensity rainfall as dominant triggers, particularly in complex mountainous areas. Overall, landslide risk in the studied tropical regions is predominantly categorized as moderate to very high. DEM effectively extracts elevation, slope, and flow parameters. Frequency Ratio and Analytic Hierarchy Process achieved the highest accuracy (Area Under Curve/AUC 0.90-0.91), followed by Random Forest (0.85). This study synthesizes tropical climate-topography findings and identifies research gaps regarding temporal rainfall integration, DEM resolution, and field validation.
Keywords: Landslide, Rainfall, Topography, Digital Elevation Model, Systematic Literature Review
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