A Forest Health Assessment Method Based on ABC-MNN

To better support forest sustainable management, this paper explores the technical framework for forest health assessment, and then focuses on how to better execute the evaluation part of this technical framework. Modular neural networks (MNN) have been shown to be more efficient for classification problems than the conventional monolithic artificial neural network. Therefore, this was used in the evaluation part of this technical framework. Unfortunately, the conventional back propagation (BP) algorithm which is commonly used to train each single artificial neural network (ANN) in MNN has slow convergence speed and the local minimum. To solve this issue, the artificial bee colony (ABC) algorithm is introduced. By combining the robust global searching ability of ABC algorithm with the strong nonlinear mapping and learning ability of BP algorithm, the hybrid ABC-BP algorithm was adopted as the learning algorithm of each single ANN in MNN. In summary, this paper presents a new model of MNN based on the hybrid ABC-BP algorithm (ABC-MNN, for short) to be used for forest health assessment. Its effectiveness is supported and illustrated in the accompanying experimental results.