An IoT Tree Health Indexing Method Using Heterogeneous Neural Network

Abstract

Urban trees provide essential ecosystem services on regulating temperature and humidity, filtering urban pollutants, and improving air quality. However, the increasing number of urban trees put pressure on maintenance and public safety. The total compensatory value of the trees, consisting of inspection, maintenance, and settlement of tree damages, is more than $2 trillion USD. At this point in time, there is no known research on manifesting guidance on automated tree health assessment. The Internet-of-Things (IoT) proliferates the deployment of wireless sensors and networks. A concept of the IoT trees is raised to implement various sensors on the trees for automated health monitoring and assessment. In this paper, an urban tree health index (UTHI) is first developed to indicate the health of urban IoT trees. The index will facilitate preventive measures on urban trees. To construct the indexing model, seven (7) dynamic (time-series) features and seven (7) static features are extracted to explore the ambient effects on urban tree health. Afterward, a heterogeneous neural network (HNN) for UTHI modeling is developed to adopt the heterogeneous feature structure. In HNN, the dynamic features are analyzed in the gated recurrent unit (GRU) layer and the static features are analyzed in a hidden layer. The novel fusion layer then aggregates the outputs computed from those layers and further explores unseen correlations among all features. The experimental result verifies that the HNN-based modeling achieves high accuracy and model fitness with the error rate of less than 5%. In addition, the HNN achieves 34% to 66% improvement of accuracy in comparison with the other machine learning algorithms. The supremacy of the developed model is that all indexing features can be predefined or monitored by the IoT sensors, thus rendering an automated and economic urban tree management.