Predicting Daily Maximum Temperatures Using Linear Regression and Eta Geopotential Thickness Forecasts

Speaker Adaptation using Maximum Likelihood Linear Regression

Modeling maximum daily temperature using a varying coefficient regression model

Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature. A good predictive model f...

Bias Removal and Model Consensus Forecasts of Maximum and Minimum Temperatures Using the Graphical Forecast Editor

Regression Model for Daily Maximum Stream Temperature

An empirical model is developed to predict daily maximum stream temperatures for the summer period. The model is created using a stepwise linear regression procedure to select significant predictors. The predictive model includes a prediction confidence interval to quantify the uncertainty. The methodology is applied to the Truckee River in California and Nevada. The stepwise procedure selects ...

Predicting groundwater levels using linear regression and neural networks

Water resources managers can benefit from accurate prediction of the availability of groundwater. In this project I present two models to predict groundwater levels in an unconfined shallow aquifer in the Searsville basin, part of the Jasper Ridge Biological Preserve. The input data (ie, features) for the models includes local weather, lake stage, and stream flow data, and moving averages of th...