A hybrid artificial neural network-based agri-economic model for predicting typhoon-induced losses

Building a model to rapidly simulate the impact of typhoons on agriculture and to predict agricultural losses is crucial and great help for remedial measure and distributing subvention right after the disaster. The relationship between typhoon-related meteorological factors and agricultural losses was first evaluated, and the Pearson’s test was applied to find consequences of both landfall and non-landfall which can be appropriately used to synthesize the possible coverage to suitably describe how typhoons influence agricultural losses. The self-organizing feature map (SOM) was then used to map similar properties of data into the same cluster and display the distribution of input–output patterns. Then, the clusters were adopted as centroids of radial basis function (RBF) neural networks. Finally, two hybrid self-organizing radial basis (SORB) networks that integrated SOM into RBF were constructed for predicting the event-based agricultural losses by feeding two different meteorological inputs (scenarios 1 and 2). The results indicate that the constructed SORB network has great ability to capture the relationship between meteorological characteristics and agricultural losses. Previously, it always takes several days to investigate and evaluate the agricultural damages after typhoons, which is a timeconsuming process. In this study, the proposed agri-economic model also demonstrates its outstanding predictability, in real-time, and therefore effectively accelerates the official decision making on agricultural compensation after a typhoon strike.