Combined Monte Carlo and Machine Learning Algorithms to Predict Horizontal Expansion of Lima City

Abstract

In this paper, the method of Monte Carlo is projected onto the Mitchell criteria inside the framework of Machine Learning. Because the probabilistic character that exhibits the theory of Mitchell, the Monte Carlo technology enters as a tool that filter all those states that are far away from the realistic expectations when rules are dictated by linear systems. The present methodology is applied to the assessment of the urbanistic expansion of Lima city at Perú. Thus, based in a probabilistic master equation it is estimated a possible geometrical shape of Lima city obtaining a rectangle shape due to the increment of habitants, jobs and new roads. The final error of hybrid model was of order or 12% (statistical).