Paper Title :Rainfall Prediction: Accuracy Enhancement for Myanmar Using Machine Learning
Author :Khine Thet Mon, Teruhiko Hiraishi
Article Citation :Khine Thet Mon ,Teruhiko Hiraishi ,
(2023 ) " Rainfall Prediction: Accuracy Enhancement for Myanmar Using Machine Learning " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 8-14,
Volume-11,Issue-8
Abstract : The designation of this project is to enhance accurate rainfall data to support weather and disaster prediction by
using advanced technological tools and to save people by reducing forecasting time and cost. Since heavy rainfall can affect
many disasters, rainfall is an important weather variable for meteorology, hydrology, and climatology. For the economy and
lifetime of humans, heavy precipitation prediction, a cause for natural disasters like floods and drought, could be a major
responsibility for the meteorology and hydrology department. Accurate prediction is useful for people to take preventive
measures. There are two types of prediction: short-term rainfall prediction and long-term rainfall prediction, mostly shortterm
predictions can give us accurate results, and building a model for long-term rainfall prediction is a challenge. The
accuracy of rainfall is extremely important for agricultural countries like Myanmar. The prediction of precipitation using
machine learning techniques may use regression and multi-class classification in this research. The fifteen years of monthly
historical weather data collected from two stations were used to train as input for learning and testing the models for
regression and four weather stations in Yangon were used for classifications. Observed weather attributes such as Pressure,
Temperature, Humidity, Wind Direction, Wind Speed, Rainfall, and Dew Point Temperature are used as input and the
output is Rainfall. According to the correlation between weather parameters, different combinations of input weather
parameters were used to build the models. In this case study, the purposes are not only for accuracy but also for time. After
assessing the accuracy and processing time results, Decision Forest performed better for regression and Neural Network is
better in multi-class classification although the process running time is not too much different by 30 minutes.
Keywords - Rainfall Prediction, Accuracy, Machine Learning, Microsoft Azure, Decision Forest, Neural Network
Type : Research paper
Published : Volume-11,Issue-8
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-20081
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Copyright: © Institute of Research and Journals
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Published on 2023-11-27 |
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