Engineering Commons^™

Spatio-Temporal Prediction Of Root Zone Soil Moisture Using Multivariate Relevance Vector Machines, B. Zaman, Mac Mckee

Civil and Environmental Engineering Faculty Publications

oot zone soil moisture at one and two meter depths are forecasted four days into the future. In this article, we propose a new multivariate output prediction approach to root zone soil moisture assessment using learning machine models. These models are known for their robustness, efficiency, and sparseness; they provide a statistically sound approach to solving the inverse problem and thus to building statistical models. The multivariate relevance vector machine (MVRVM) is used to build a model that forecasts soil moisture states based upon current soil moisture and soil temperature conditions. The methodology combines the data at different depths from …