Hydrologic Predictions using Probabilistic Relational Models
نویسندگان
چکیده
The US Army faces a significant burden in planning sustainment operations. Currently, logistics planners must manually evaluate potential emplacement sites to determine their terrain suitability. Sites subject to rainfall-runoff responses such as flooding are ill-suited for emplacements, but evaluating the likelihood of such responses requires significant time and expertise. To reduce the time and to ease the difficulty of logistics site selection we demonstrated a series of Terrain Impact Decision Extensions (TIDE) for use in logistics planning tools and processes. TIDE performs data-fusion over a variety of terrain and weather data sets using probabilistic relational models (PRMS), providing a high-performance alternative to physics-based hydrologic models.
منابع مشابه
Learning Probabilistic Models of Relational Structure
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the relational structure. The recently introduced framework of probabilistic relational models (PRMs) allows us to represent probabilistic models over multiple entities that utilize the relations be...
متن کاملDecision-Driven Models with Probabilistic Soft Logic
We introduce the concept of a decision-driven model, a probabilistic model that reasons directly over the uncertain information of interest to a decision maker. We motivate the use of these models from the perspective of personalized medicine. Decision-driven models have a number of benefits that are of particular value in this domain, such as being easily interpretable and naturally quantifyin...
متن کاملAutomatic Calibration of HEC-HMS Model Using Multi-Objective Fuzzy Optimal Models
Estimation of parameters of a hydrologic model is undertaken using a procedure called “calibration” in order to obtain predictions as close as possible to observed values. This study aimed to use the particle swarm optimization (PSO) algorithm for automatic calibration of the HEC-HMS hydrologic model, which includes a library of different event-based models for simulating the rainfall-runoff pr...
متن کاملSequential data assimilation for streamflow forecasting using a distributed hydrologic model: particle filtering and ensemble Kalman filtering
Accurate streamflow predictions are crucial for mitigating flood damage and addressing operational flood scenarios. In recent years, sequential data assimilation methods have drawn attention due to their potential to handle explicitly the various sources of uncertainty in hydrologic models. In this study, we implement two ensemble-based sequential data assimilation methods for streamflow foreca...
متن کاملLearning Probabilistic Models of Link Structure
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the link structure. The recently introduced framework of probabilistic relational models (PRMs) embraces the...
متن کامل