000			02271nam a22001817a 4500
003			OSt
005			20240828141801.0
008			240820b |||||||| |||| 00| 0 eng d
040			_aMMSU _cULS
100			_aAdaon, Ma. Arlena Q...et.al.
245			_aLandslide susceptibility assessment of Pagudpud, Ilocos Norte along the Manila North Road / _cMa. Arlena Q. Adaon, Chery F. Cabuyadao, Laureigh Karyz T. Dalire, John Daniel D. Pagdilao
260			_aCity of Batac : _bMMSU, _c2024.
300			_axviii, 95 leaves : _c28 cm
500			_aUTHESIS (Bachelor of Science in Civil Engineering)
504			_aBibliography: leaves 64 to 67
520			_aLandslides frequently disrupt transportation and damage infrastructure in Pagudpud, Ilocos Norte, particularly along the Manila North Road. This study utilized logistic regression to analyze the relationship between landslides and various causal factors, including slope angle, slope aspect, plan curvature, profile curvature, elevation, TWI, SPI, STI, land cover, distance to stream, distance to fault, distance to road, soil type, and precipitation. A Digital Elevation Model was created to extract these factors, identifying elevation, slope angle, plan curvature, distance to faults, and precipitation as the most significant contributors to landslide occurrence, with p-values below 0.05.A landslide susceptibility map was then generated, categorizing areas into low, moderate, high, and very high susceptibility zones. Notably, areas in Balaoi and Caunayan showed moderate susceptibility despite gentle slopes, while large portions of Tarrag, Subec, and Caunayan were highly susceptible, and parts of Balaoi and Pasaleng were very high susceptible. The model's validity was confirmed with an ANOVA p-value of 9.2536E-10 which is well below a threshold of significance 0.05. Based on the susceptibility map, a five-year management plan was developed, detailing specific countermeasures for each zone to mitigate landslide risks. This study provides crucial information for reducing landslide risks and protecting lives and infrastructure in Pagudpud by combining field surveys, historical landslide information, and logistic regression analysis, aiding future decision-making and regional safety.
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