Use of artificial neural networks for predicting rice crop damage by greater flamingos in the Camargue, France

Abstract Since the 1980s, incursions of greater flamingo ( Phoenicopterus ruber roseus ) in rice fields have been reported almost every year in the Camargue, south-eastern France, and more recently in Spain. We assessed the performances of artificial neural networks (ANN) in predicting presence or absence of flamingo damages from 11 variables describing landscape features of rice paddies. The global matrix of 1978 records (276 with damage and 1702 without) for the 1993–1996 period was used to determine the suitable parameters: number of hidden layer nodes and number of iterations. In order to avoid particular inputs either in the training set or in the testing set, ten different randomly sampled training sets were available. A classic multilayer feed-forward neural network with back-propagation algorithm was used throughout these experiments. Data from 1993 to 1996 were used to predict data for 1997 (73 fields with damage and 1905 without) and 1998 (88 with damage and 1890 without). Three training set compositions were displayed: (I) the whole data set (1978 observations), (II) an equal number (276) of damaged and undamaged fields (552 observations), (III) a set with 1/3 of observations being damaged fields (276) and 2/3 undamaged (552). ANN faced some difficulty in predicting both presence and absence of damage. The number of each type record in the training set was particularly sensitive. ANN predicted the more frequent outcome, (i.e. absence of damage). Most often, better results were obtained when equilibrating the number of presences and absences. In this case, we obtained performances ranging from 64% up to 87% according to the presence and absence of data in the training set. When fitting ANN with the whole set of presences to predict damage 1 year later, these results stabilised at ≈79% for 1997 and between 66 and 72% for 1998 when more than half of the damaged fields were never visited by flamingos during the period 1993–1997. Our performances are quite similar to the results obtained by previous authors and predictability from 1 year to the following one also supports that ANN can be an alternative or a supplement to actual scaring methods in identifying potential damaged fields and propose agricultural management plans or concentrate scaring actions on these high-risk areas.