Ubiquitous Limited Sensor-based Weather Binary Prediction Network Using Linear and Nonlinear Fittings and 14-gene Genetic Expression
Tyrone Ashley Go, Jose Antonio Cadavillo, Joyce Yuenlam Cai, Dmitri Chuacuco, Jonah Jahara Baun, Argel Alejandro Bandala, Ronnie Concepcion · 2022
Temperature and humidity are two of the many factors that play vital roles in weather, and these two factors are used in determining the present weather conditions. Not only does it concern meteorology, but especially the food science and agricultural fields. Weather monitoring pertains to an activity in which the state of the atmosphere is analyzed, which usually includes the variables such as wind speed, temperature, humidity, air moisture, pressure, and rainfall. This study uses the Arduino Uno board as a microcontroller and the DHT11 temperature (T) and humidity (H) sensor to gather information about the environment and display it in the LCD module. Simple linear, Gauss-Newton and Nernst-based non-linear, and 14-gene genetic programming regression models were developed and embedded to motes in four selected rain test areas in Metro Manila and Rizal province in predicting two weather states (no rain and raining). The expected result in this system is an approximation as to whether or not it would rain based on the data gathered throughout the project development. Weather data were automatically uploaded and stored in a ThingSpeak server using ESP32, which is viewed in the form of a graph. Based on the results, the temperature changes slightly during rainfall while humidity; on the other hand, changes much more drastically during rainfall and is a key telltale sign of rainfall. Linear regression outperformed other models in binary rain prediction based on temperature and humidity parameters only.