Efficient Visual Ego-Motion Estimation for Agile Flying Robots

Micro air vehicles (MAVs) have shown significant potential in modern society. The development in robotics and automation is changing the roles of MAVs from remotely controlled machines requiring human pilots to autonomous and intelligent robots. There is an increasing number of autonomous MAVs involved in outdoor operations. In contrast, the...

Bayesian system identification for structures considering spatial and temporal correlation

The decreasing cost and improved sensor and monitoring system technology (e.g., fiber optics and strain gauges) have led to more measurements in close proximity to each other. When using such spatially dense measurement data in Bayesian system identification strategies, the correlation in the model prediction error can become significant. The...

Arrival Management Support in the Presence of Prediction Uncertainty

Future concepts for Air Traffic Management (ATM) foresee—and even mandate—an increase in the distance from the destination airport at which the ATM system should start planning the arrival sequence and arrival time of inbound flights. By increasing the horizon beyond one hour before arrival, it becomes possible to timely adjust the flight path...

A combined forecasting and packing model for air cargo loading: A risk-averse framework

In this paper, we present a combined forecasting and optimization decision-support tool to assist air cargo revenue management departments in the acceptance/rejection process of incoming cargo bookings. We consider the case of a combination airline and focus on the passenger aircraft belly capacity. The process is dynamic (bookings are received...

Hydrological Interpretation of a Statistical Measure of Basin Complexity

This paper studies how streamflow predictability varies with basin characteristics. We introduce an index of basin complexity that is based on a model of least statistical complexity that is needed to reliably predict daily streamflow of the basin. We then relate it with climate, vegetation and soil characteristics of the basin. Daily...

Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine

Two statistical post-processing approaches for estimation of predictive hydrological uncertainty are compared: (i) ‘dressing’ of a deterministic forecast by adding a single, combined estimate of both hydrological and meteorological uncertainty and (ii) ‘dressing’ of an ensemble streamflow forecast by adding an estimate of hydrological...

Estimating real-time predictive hydrological uncertainty

Flood early warning systems provide a potentially highly effective flood risk reduction measure. The effectiveness of early warning, however, is affected by forecasting uncertainty: the impossibility of knowing, in advance, the exact future state of hydrological systems. Early warning systems benefit from estimation of predictive uncertainties,...

Performance Impact of Improved Departure Time Prediction Relative to Sector Demand & Arrival Time Predictability

This thesis deals with traffic forecasts of Airspace Users for Air Navigation Service Providers. Currently there is a high amount of uncertainty in the traffic forecast. Air Traffic Flow Managers anticipate for unforeseen traffic by increasing the forecasted maximum capacity threshold by more than 10%. The European ATM research program SESAR...

Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of models. One of multi modelling approaches called ...

On hydrological model complexity, its geometrical interpretations and prediction uncertainty

Knowledge of hydrological model complexity can aid selection of an optimal prediction model out of a set of available models. Optimal model selection is formalized as selection of the least complex model out of a subset of models that have lower empirical risk. This may be considered equivalent to minimizing an upper bound on prediction error,...

Optimal Control of Water Systems Under Forecast Uncertainty: Robust, Proactive, and Integrated

Water systems consist of natural and man-made objects serving multiple essential purposes. They are affected by many types of meteorological disturbances. In order to deal with these disturbances and to serve the desired objectives, infrastructures have been built and managed by societies for specific purposes. Given a water system, and its...

A decision support system for use of probability forecasts

Often, water management decisions are based on hydrological forecasts, which are affected by inherent uncertainties. It is increasingly common for forecasters to make explicit estimates of these uncertainties. Associated benefits include the decision makers’ increased awareness of forecasting uncertainties and the potential for risk-based...

Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments

Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread-skill relationship of forecast ensembles. The BMA predictive probability density function (pdf) of any quantity of...

Model complexity control for hydrologic prediction

A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore needed. We compare three model complexity control...