Masaaki Imanaga

An Application of Neurogenetic Algorithm System to Individual Tree Growth Model

A neural network system with genetic algorithms (Neurogenetic Algorithm System, or NGAS) was employed to develop individual coniferous tree growth models. A multivariable regression model was applied to compare the performance of NGAS. An IBM personal computer with the BioComp System’s software program of NGO was used to execute this comparison. The results indicate that NGAS is more accurate and effective than the conventional regression method in modeling individual tree growth based on the criteria of Sum of Squared Error (SSE), Average Absolute Error (AAE), Mean Squared Error (MSE) and Final Predicted Error (FPE). This study also suggests that individual tree growth may indeed be a non-linear process. Using this flexible neural network system to model individual tree growth can yield satisfactory prediction results.

Japanese Attitudes Towards Forests According to Comparative Opinion Surveys

The aim of this study was to discover what the residents of various countries think and feel about their forests. Large scale opinion surveys had been conducted throughout the world since 1979 to 1996 in various Japanese cities, four German cities, one French city, three Brazilian cities, and in three Peruvian cities. Results of the opinion surveys were analyzed, and attitudes towards forest and nature summarized as follows; 1. Many people believed that a spirit exists in natural things animism and had a quite deep emotional affinity with forest and nature except in Tokyo. 2. On the ethical behavior of forest management, positive responses were most frequent in Germany and France, followed by Brazil and Peru, and then Japan.