Use of an Artificial Neural Network to Predict Population Dynamics of the Forest–Pest Pine Needle Gall Midge (Diptera: Cecidomyiida)

Abstract 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.

[1]  William W. S. Wei,et al.  Time series analysis - univariate and multivariate methods , 1989 .

[2]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[3]  Paul J. Werbos,et al.  The roots of backpropagation , 1994 .

[4]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[5]  Peter Turchin,et al.  Complex Dynamics in Ecological Time Series , 1992 .

[6]  Gerrit Hoogenboom,et al.  Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean , 1994 .

[7]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[8]  Gérard Boudjema,et al.  Revealing dynamics of ecological systems from natural recordings , 1996 .

[9]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[10]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[11]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[12]  Edgardo A. Ferrán,et al.  Clustering proteins into families using artificial neural networks [published erratum appears in Comput Appl Biosci 1992 Jun;8(3): 305] , 1992, Comput. Appl. Biosci..

[13]  Gerrit Kateman,et al.  Two-dimensional mapping of IR spectra using a parallel implemented self-organising feature map , 1993 .

[14]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[15]  C. L. Giles,et al.  Dynamic recurrent neural networks: Theory and applications , 1994, IEEE Trans. Neural Networks Learn. Syst..

[16]  Fred E. Smeins,et al.  Predicting grassland community changes with an artificial neural network model , 1996 .

[17]  H. Lohninger,et al.  Comparing the performance of neural networks to well-established methods of multivariate data analysis: the classification of mass spectral data , 1992 .

[18]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[19]  K. Soné.  Population dynamics of the pine needle gall midge, Thecodiplosis japonensis Uchida et Inouye (Diptera, Cecidomyiidae) , 1987 .

[20]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[21]  Sun-Yuan Kung,et al.  Digital neural networks , 1993, Prentice Hall Information and System Sciences Series.

[22]  Young-Seuk Park,et al.  Patternizing communities by using an artificial neural network , 1996 .