Input-Features for Water Quality Estimation in Lake Hosenko, Japan Using Neural Network

Abstract

Tamagawa river in Akita, Japan, has possibility to impact surrounding environments because an inflow of acidic water including hydrous ferric oxide, arsenic compound, and others. Our previous studies were working for water quality analysis using satellite remote sensing techniques to Lake Hosenko has water source from Tamagawa river. Concretely, fuzzy c-means clustering was used for creating estimation maps of water quality. As the results, the FCM was useful for understanding the pollution due to hydrous ferric oxide in the lake. However, the previous studies used only satellite data for creation of estimation map. Therefore, the purpose of this study is to discuss how to specifically obtain state information of water quality and to develop a method for creating estimation map of water quality using neural network with satellite data and topographical information of Lake Hosenko such as elevation as input-features. In this paper, water quality maps were created using each band of satellite data and elevation of Lake Hosenko as input-features.