Desarrollo de una aplicación móvil para predecir las precipitaciones fluviales en la ciudad de Quito utilizando el algoritmo Random Forest

Abstract

Like numerous other urban areas, Quito has issues with rainfall. This precipitation may result in landslides, floods, and other natural disasters that put lives in danger and significantly harm property. The City is unable to respond to these events and prepare for them in a sufficient manner due to the lack of an efficient river precipitation forecasting system. Conventional weather forecasting techniques frequently fall short in terms of accuracy or are not tailored to Quito's unique characteristics. Recommended to solve this problem, a mobile application should be created that employs the Random Forest algorithm to forecast river precipitation in the city. A machine learning method called the Random Forest algorithm combines several predictive models to increase the precision of predictions. The temperature, humidity, air pressure, and other real- time weather information would be gathered by this mobile app. and incorporate it into the Random Forest algorithm as input. Fluvial precipitation forecasts could be produced by the application with more accuracy and foresight. The mobile application would improve planning and decision- making for emergency management, preventive evacuations, and other disaster mitigation measures by giving Quito citizens and local authorities more precise information on potential river rainfall.