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

Authors : Engin PEKEL

Pages : 186-194

Doi:10.28948/ngumuh.529418

View : 22 | Download : 21

Publication Date : 2020-01-30

Article Type : Research Paper

Abstract :Soil plays a vital role in the climate system. This paper performs a hybrid methodology that consists of particle swarm optimization insert ignore into journalissuearticles values(PSO); and artificial neural network insert ignore into journalissuearticles values(ANN); to estimate soil moisture insert ignore into journalissuearticles values(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.
Keywords : estimation, artificial neural network, particle swarm optimization, soil moisture