EVALUATION OF ESTIMATION PERFORMANCE FOR SOIL MOISTURE USING PARTICLE SWARM OPTIMIZATION AND ARTIFICIAL NEURAL NETWORK

Yıl 2020, , 186 - 194, 30.01.2020

Engin Pekel

https://doi.org/10.28948/ngumuh.529418

Öz

Soil plays a vital role in the climate system.
This paper performs a hybrid methodology that consists of particle swarm optimization
(PSO) and artificial neural network (ANN) to estimate soil moisture (SM) by
considering different parameters that include air temperature, time, relative
humidity and soil temperature. Besides, this paper investigates the effects of
the parameters of PSO-ANN by utilizing from the response surface. PSO algorithm
is involved in the process of changing the weights of ANN. The coefficient of
determination and mean absolute error are chosen to measure the performance of
the performed hybrid PSO-ANN. The numerical results show that hybrid PSO-ANN is
applied to estimate SM successfully.

Anahtar Kelimeler

estimation, artificial neural network, particle swarm optimization, soil moisture

Kaynakça

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