Multivariate Bayesian Machine Learning Regression for Operation and Management of Multiple Reservoir, Irrigation Canal, and River Systems

The principal objective of this dissertation is to develop Bayesian machine learning models for multiple reservoir, irrigation canal, and river system operation and management. These types of models are derived from the emerging area of machine learning theory; they are characterized by their ability to capture the underlying physics of the system simply by examination of the measured system inputs and outputs. They can be used to provide probabilistic predictions of system behavior using only historical data. The models were developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a sparse Bayesian learning machine approach for regression. Using this Bayesian approach, a predictive confidence interval is obtained from the model that captures the uncertainty of both the model and the data. The models were applied to the multiple reservoir, canal and river system located in the regulated Lower Sevier River Basin in Utah. The models were developed to perform predictions of multi-time-ahead releases of multiple reservoirs, diversions of multiple canals, and streamflow and water loss/gain in a river system. This research represents the first attempt to use a multivariate Bayesian learning regression approach to develop simultaneous multi-step-ahead predictions with predictive confidence intervals for multiple outputs in a regulated river basin system. These predictions will be of potential value to reservoir and canal operators in identifying the best decisions for operation and management of irrigation water supply systems

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices) has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat) imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM) to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed. © 2016 by the authors

Topsoil Moisture Estimation for Precision Agriculture Using Unmanned Aerial Vehicle Multispectral Imagery

There is an increasing trend in crop production management decisions in precision agriculture based on observation of high resolution aerial images from unmanned aerial vehicles (UAV). Nevertheless, there are still limitations in terms of relating the spectral imagery information to the agricultural targets. AggieAir™ is a small, autonomous unmanned aircraft which carries multispectral cameras to capture aerial imagery during pre-programmed flights. AggieAir enables users to gather imagery at greater spatial and temporal resolution than most manned aircraft and satellite sources. The platform has been successfully used in support of a wide variety of water and natural resources management areas. This paper presents results of an on-going research in the application of the imagery from AggieAir in the remote sensing of top soil moisture estimations for a large field served by a center pivot sprinkler irrigation system

A Robust Monthly Streamflow Forecasting Model Using a Multivariate Bayesian Regression Model Coupled with Wavelet Decomposition Approach

This research presents a modeling approach that incorporates wavelet-based analysis techniques used in statistical signal processing and multivariate machine learning regression to forecast monthly streamflow. The streamflows from two USGS gauge stations located on the Lake Fork River and Yellowstone River in the Uinta Basin, Utah are used. The model is developed using a Multivariate Relevance Vector Machine (MVRVM) that is based on a Bayesian learning machine approach for regression. The inputs of the model utilize past information of streamflow and Pacific sea surface temperature (SST). The inputs are decomposed into meaningful components formulated in terms of wavelet multiresolution analysis (MRA). The proposed hybrid of wavelet decomposition and machine learning regression approaches captures sufficient information at meaningful temporal scales and improves the performance of the monthly streamflow forecasts in Utah. A bootstrap analysis is used to explore the robustness of the hybrid modeling approach

Wavelet-based cross-correlation analysis and a hybrid wavelet-multivariate Bayesian model for short-term streamflow forecasting using local climatic data

A new approach is presented for creating short-term forecasts of streamflow in a snowmelt-dominated watershed. The forecasting methodology relies on (1) wavelet-based cross-correlation analysis to study lead-lag relationships between streamflow and climatic time series data and (2) incorporation of the resulting information into a multivariate Bayesian regression model to develop short-term streamflow forecasts. The wavelet-based cross-correlation analysis is demonstrated with the use of daily local climatic data (in the form of precipitation, temperature and snow water equivalent) that are obtained from automated snowpack telemetry (SNOTEL) stations in the Bear River Watershed in Utah. Daily streamflow data is obtained from a U.S. Geological Survey (USGS) gage station located on the Logan River, near Logan, Utah. The data are decomposed into meaningful components formulated in terms of wavelet multiresolution analysis. Next, a computational intelligence modeling approach based on a multivariate Bayesian regression is used to produce daily streamflow forecasts up to seven days ahead. The results from the wavelet-based cross-correlation analysis are used to select the data to build the model. The proposed methods can incorporate important information from trends of the local climate time series into models that learn these patterns to produce improved streamflow predictions at different time scales

Multivariate Bayesian regression approach to forecast releases from a system of multiple reservoirs

This research presents a model that simultaneously forecasts required water releases 1 and 2 days ahead from two reservoirs that are in series. In practice, multiple reservoir system operation is a difficult process that involves many decisions for real-time water resources management. The operator of the reservoirs has to release water from more than one reservoir taking into consideration different water requirements (irrigation, environmental issues, hydropower, recreation, etc.) in a timely manner. A model that forecasts the required real-time releases in advance from a multiple reservoir system could be an important tool to allow the operator of the reservoir system to make better-informed decisions for releases needed downstream. The model is developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a sparse Bayesian regression model approach. With this Bayesian approach, a predictive confidence interval is obtained from the model that captures the uncertainty of both the model and the data. The model is applied to the multiple reservoir system located in the Lower Sevier River Basin near Delta, Utah. The results show that the model learns the input–output patterns with high accuracy. Computing multiple-time-ahead predictions in real-time would require a model which guarantees not only good prediction accuracy but also robustness with respect to future changes in the nature of the inputs data. A bootstrap analysis is used to guarantee good generalization ability and robustness of the MVRVM. Test results demonstrate good performance of predictions and statistics that indicate robust model generalization abilities. The MVRVM is compared in terms of performance and robustness with another multiple output model such as Artificial Neural Network (ANN)

Forecasting Agricultural Commodity Prices Using Multivariate Bayesian Machine

The purpose of this paper is to perform multiple predictions for agricultural commodity prices (one, two and three month periods ahead). In order to obtain multiple-time-ahead predictions, this paper applies the Multivariate Relevance Vector Machine (MVRVM) that is based on a Bayesian learning machine approach for regression. The performance of the MVRVM model is compared with the performance of another multiple output model such as Artificial Neural Network (ANN). Bootstrapping methodology is applied to analyze robustness of the MVRVM and ANN

Adjusting wavelet-based multiresolution analysis boundary conditions for long-term streamflow forecasting

We propose a novel technique for improving a long-term multi-step-ahead streamflow forecast. A model based on wavelet decomposition and a multivariate Bayesian machine learning approach is developed for forecasting the streamflow 3, 6, 9, and 12 months ahead simultaneously. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model accuracy can be increased by using the wavelet boundary rule introduced in this study. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data from the Yellowstone River in the Uinta Basin in Utah. The model based on the combination of wavelet and Bayesian machine learning regression techniques is compared with that of the wavelet and artificial neural networks-based model. The robustness of the models is evaluated. Copyright © 2015 John Wiley & Sons, Ltd