THIS STUDY ADDRESSES THE IMPORTANCE OF CONDUCTING MASS MOVEMENT SUSCEPTIBILITY MAPPING AND HAZARD ASSESSMENT USING QUANTITATIVE TECHNIQUES, INCLUDING MACHINE LEARNING, IN THE NORTHERN LIMA COMMONWEALTH (NLC). A PREVIOUS EXPLORATION OF THE TOPOGRAPHIC VARIABLES REVEALED A HIGH CORRELATION AND MULTICOLLINEARITY AMONG SOME OF THEM, WHICH LED TO DIMENSIONALITY REDUCTION THROUGH A PRINCIPAL COMPONENT ANALYSIS (PCA). SIX SUSCEPTIBILITY MODELS WERE GENERATED USING WEIGHTS OF EVIDENCE, LOGISTIC REGRESSION, MULTILAYER PERCEPTRON, SUPPORT VECTOR MACHINE, RANDOM FOREST, AND NAIVE BAYES METHODS TO PRODUCE QUANTITATIVE SUSCEPTIBILITY MAPS AND ASSESS THE HAZARD ASSOCIATED WITH TWO SCENARIOS: THE FIRST BEING EL NIÑO PHENOMENON AND THE SECOND BEING AN EARTHQUAKE EXCEEDING 8.8 MW. THE MAIN FINDINGS INDICATE THAT MACHINE LEARNING MODELS EXHIBIT EXCELLENT PREDICTIVE PERFORMANCE FOR THE PRESENCE AND ABSENC...