The backpropagation algorithm in artificial neural networks was used to forecast dynamic data of a forest pest population of the pine needle gall midge, Thecodiplosis japonensis Uchida et Inouye, a serious pest in pine trees in northeast Asia. Data for changes in population density were sequentially given as input, whereas densities of subsequent samplings were provided as matching target data for training of the network. Convergence was reached, generally after 20,000 iterations with learning coefficients of 0.5–0.8. When new input data were given to the trained network, recognition was possible and population density at the subsequent sampling time could be predicted.
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1 December 2000
Use of an Artificial Neural Network to Predict Population Dynamics of the Forest–Pest Pine Needle Gall Midge (Diptera: Cecidomyiida)
Tae-Soo Chon,
Young-Seuk Park,
Ja-Myung Kim,
Buom-Young Lee,
Yeong-Jin Chung,
YooShin Kim
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artificial neural network
backpropagation algorithm
pine needle gall midge
population dynamics
prediction
Thecodiplosis japonensis