Prediction of Land Subsidence due to Ground‐Water Use in Snow Country

Abstract

Prediction of land subsidence in a heavy‐snowfall area is one of the most important problems in the determination of optimum use of ground water for melting snow. In this study, land subsidence is predicted by using a regression‐equation model that includes past land subsidence, ground‐water level, and snow‐fall. The components of the long‐period variation and the short‐period variation in land subsidence are predicted by the parameters using the least‐square method and Kaiman filtering, respectively. The memory length of the regression‐equation model is justified by comparing the value of Akaike's information criterion. The proposed method has accurately predicted land subsidence one and two months ahead in snow country in Japan. The input data for this prediction model (regression equations) are past land subsidence, ground‐water level, and snowfall. This method to predict land subsidence is applicable to any snow country in the world.