METHODS AND SYSTEMS FOR FACILITATING FORECASTING OF IN-SITU ENVIRONMENTAL CONDITIONS USING NONLINEAR ARTIFICIAL NEURAL NETWORKS-BASED MODELS

摘要

Disclosed herein is a method for facilitating forecasting of in-situ environmental conditions using nonlinear artificial neural networks-based models. Accordingly, the method may include receiving weather forecast model data, environmental data, and in-situ environmental data from an external device, analyzing the weather forecast model data, the environmental data, and the in-situ environmental data, generating input data, training a nonlinear machine learning-based in-situ environmental forecasting model based on the input data using a machine learning technique, validating the nonlinear machine learning-based in-situ environmental forecasting model using the in-situ environmental data, updating the nonlinear machine learning-based in-situ environmental forecasting model, generating an updated nonlinear machine learning-based in-situ environmental forecasting model, generating an in-situ forecast for an in-situ environmental condition, transmitting the in-situ forecast to a user device, and storing the nonlinear machine learning-based in-situ environmental forecasting model and the updated nonlinear machine learning-based in-situ environmental forecasting model.