Ubiquitous Limited Sensor-based Weather Binary Prediction Network Using Linear and Nonlinear Fittings and 14-gene Genetic Expression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Go:2022:HNICEM,
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author = "Tyrone Ashley Go and Jose Antonio Cadavillo and
Joyce Yuenlam Cai and Dmitri Chuacuco and
Jonah Jahara Baun and Argel Bandala and Ronnie Concepcion",
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booktitle = "2022 IEEE 14th International Conference on Humanoid,
Nanotechnology, Information Technology, Communication
and Control, Environment, and Management (HNICEM)",
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title = "Ubiquitous Limited Sensor-based Weather Binary
Prediction Network Using Linear and Nonlinear Fittings
and 14-gene Genetic Expression",
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year = "2022",
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abstract = "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 analysed, 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.",
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keywords = "genetic algorithms, genetic programming, Temperature
sensors, Temperature distribution, Rain,
Microcontrollers, Humidity, Predictive models, Liquid
crystal displays, embedded system, environment
monitoring, rain prediction, ubiquitous system, weather
detection system",
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DOI = "doi:10.1109/HNICEM57413.2022.10109402",
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ISSN = "2770-0682",
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month = dec,
-
notes = "Also known as \cite{10109402}",
- }
Genetic Programming entries for
Tyrone Ashley Go
Jose Antonio Cadavillo
Joyce Yuenlam Cai
Dmitri Chuacuco
Jonah Jahara Garcia Baun
Argel A Bandala
Ronnie S Concepcion II
Citations