Stream Flow Prediction by Remote Sensing and Genetic Programming

A genetic programming (GP)-based, nonlinear modeling structure relates soil moisture with synthetic-apertureradar (SAR) images to present representative soil moisture estimates at the watershed scale. Surface soil moisture measurement is difficult to obtain over a large area due to a variety of soil permeability values and soil textures. Point measurements can be used on a smallscale area, but it is impossible to acquire such information effectively in largescale watersheds. This model exhibits the capacity to assimilate SAR images and relevant geoenvironmental parameters to measure soil moisture. In the past, spaceborne radar imaging satellites used all-weather observation, but estimation methods of soil moisture based on active or passive satellite images remains uncertain. Estimation of soil moisture based on SAR measurement was made possible by developing linear regression models and nonlinear regression models in a single land use/land cover from several hundred square meters to several square kilometers, based on traditional statistical regression theory. This GP-based artificial intelligence mode uses an evolutionary computational approach to estimate soil moisture with a variety of land use/land cover patterns.