Rainfall prediction using artificial neural network with historical weather data as supporting parameters
DOI:
https://doi.org/10.26555/jifo.v16i2.a25422Abstract
Changing climatic patterns are caused by changes in variables, such as rainfall
and air temperature that occur continuously in the long term. Rainfall itself is
influenced by several weather factors such as air humidity, wind speed, air
pressure, and temperature. This study experimented to test a combination of
9 additional weather parameters such as dew point, wind gusts, cloud cover,
humidity, rainfall, air pressure, air temperature, wind direction, and wind
speed to predict daily rainfall for one year using the main parameters of the
rainfall time series. Prediction is done using Artificial Neural Network
(ANN). The ANN architecture used is to use 3 to 11 input parameters, 1
hidden layer totaling 60 neurons with the ReLu activation function, and 1
neuron in the output layer without an activation function. ANN without
additional weather parameters obtained an MSE of 0.01654, while prediction
using additional weather parameters obtained an MSE of 0.00884. So the
combination of rainfall time series parameters with additional weather
parameters is proven to provide a smaller MSE value
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