A SENSITIVITY ANALYSIS OF RIVER ENVIRONMENT FACTORS THROUGH DEEP LEARNING

The water environment of the most important watersheds of Japan generally have not improved in a considerable manner in the last two decades although central and local governments have made considerable management and improvement efforts, such as increasing sewerage system coverage rates nationwide and installing advanced wastewater treatment systems. It is believed that the marginal effects of these direct efforts have been diminishing. This study seeks to discover the most effective water environment improvement measures in a wider range other than those direct measures. An artificial intelligence (AI) model has been constructed with Deep Learning technology by applying the watershed information from 104 watersheds as teacher data to train the AI model. The well-trained AI model is used to identify the effectiveness of all the direct and indirect water-environment-related factors, ranging from geological/geographical factors, hydrological/hydraulic factors to socio-economic factors. This study concludes by pointing out that Deep Learning through big data can reveal and simulate the complicated relationships between river management goals and diverse water environment factors. It is hoped that this study will contribute to establishing a more reliable river environment planning and management methodology.