Advanced Bayesian networks for reliability and risk analysis in geotechnical engineering

The stability and deformation problems of soil have been a research topic of great concern since the past decades. The potential catastrophic events are induced by various complex factors, such as uncertain geotechnical conditions, external environment, and anthropogenic influence, etc. To prevent the occurrence of disasters in geotechnical engineering, the main purpose of this study is to enhance the Bayesian networks (BNs) model for quantifying the uncertainty and predicting the risk level in solving the geotechnical problems. The advanced BNs model is effective for analyzing the geotechnical problems in the poor data environment. The advanced BNs approach proposed in this study is applied to solve the stability of soil slopes problem associated with the specific-site data. When probabilistic models for soil properties are adopted, enhanced BNs approach was adopted to cope with continuous input parameters. On the other hand, Credal networks (CNs), developed on the basis of BNs, are specially used for incomplete input information. In addition, the probabilities of slope failure are also investigated for different evidences. A discretization approach for the enhanced BNs is applied in the case of evidence entering into the continuous nodes. Two examples implemented are to demonstrate the feasibility and predictive effectiveness of the BNs model. The results indicate the enhanced BNs show a precisely low risk for the slope studied. Unlike the BNs, the results of CNs are presented with bounds. The comparison of three different input information reveals the more imprecision in input, the more uncertainty in output. Both of them can provide the useful disaster-induced information for decision-makers. According to the information updating in the models, the position of the water table shows a significant role in the slope failure, which is controlled by the drainage states. Also, it discusses how the different types of BNs contribute to assessing the reliability and risk of real slopes, and how new information could be introduced in the analysis. The proposed models in this study illustrate the advanced BN model is a good diagnosis tool for estimating the risk level of the slope failure. In a follow-up study, the BNs model is developed based on its potential capability for the information updating and importance measure. To reduce the influence of uncertainty, with the proposed BN model, the soil parameters are updated accurately during the excavation process, and besides, the contribution of epistemic uncertainty from geotechnical parameters to the potential disaster can be characterized based on the developed BN model. The results of this study indicate the BNs model is an effective and flexible tool for risk analysis and decision making support in geotechnical engineering

HSPF Modeling of Nonpoint Sources in Tickfaw River Watershed

The Tickfaw watershed is located in southeastern Louisiana with the Tickfaw River originating in Southern Mississippi, flowing through St. Helena and Livingston Parishes, and eventually emptying into Lake Maurepas. The total drainage area is 1,896 km2. Forests cover 66% of the watershed and agriculture is the second predominant land use type. The elevation of the watershed changes from 0 m above sea level in the south to 130 m in the north. According to the 2004 Louisiana Water Quality Inventory report section 303(d), outstanding natural resource and secondary contact recreation designated uses are fully supported, but fish and wildlife propagation and primary contact recreation are not supported. According to the 303(d) list, the impairments in Tickfaw River are mercury, total dissolved solids, fecal coliform, phosphorus and dissolved oxygen. There are many suspected sources of impairment, including agriculture, construction, forest management, and industrial sources. The goal of this study is to make use of a Geographic Information System (GIS), the EPA\u27s BASINS tools, and the HSPF water quantity and quality modeling program to quantify and differentiate the sources of pollution that arise from storm water runoff coming from agriculture, forestry, and other sources. This will allow the Louisiana Department of Environmental Quality (LADEQ) personnel to better focus implementation efforts on those areas and practices that appear most critical to water quality problems. In the process, a water quality model has been calibrated and validated for annual flows; seasonal flows and for water quality parameters like dissolved oxygen, nitrogen and phosphorus. An assessment analysis was performed to determine the loading of nitrogen and phosphorus coming from each land use. Various land use scenarios were created in Tickfaw watershed and total loading resulting from these landuses were integrated with the watershed’s subbasins in the GIS for graphical presentation. These landuse scenarios were also ranked based on its resultant total loading. Based on these loading rates, total loading of nitrogen and phosphorus resulting from these land use scenarios were significantly higher when current landuse was converted to cropland and pasture, thereby adversely affecting the water quality in rivers

Advocating better habitat use and selection models in bird ecology

Studies on habitat use and habitat selection represent a basic aspect of bird ecology, due to its importance in natural history, distribution, response to environmental changes, management and conservation. Basically, a statistical model that identifies environmental variables linked to a species presence is searched for. In this sense, there is a wide array of analytical methods that identify important explanatory variables within a model, with higher explanatory and predictive power than classical regression approaches. However, some of these powerful models are not widespread in ornithological studies, partly because of their complex theory, and in some cases, difficulties on their implementation and interpretation. Here, I describe generalized linear models and other five statistical models for the analysis of bird habitat use and selection outperforming classical approaches: generalized additive models, mixed effects models, occupancy models, binomial N-mixture models and decision trees (classification and regression trees, bagging, random forests and boosting). Each of these models has its benefits and drawbacks, but major advantages include dealing with non-normal distributions (presence-absence and abundance data typically found in habitat use and selection studies), heterogeneous variances, non-linear and complex relationships among variables, lack of statistical independence and imperfect detection. To aid ornithologists in making use of the methods described, a readable description of each method is provided, as well as a flowchart along with some recommendations to help them decide the most appropriate analysis. The use of these models in ornithological studies is encouraged, given their huge potential as statistical tools in bird ecology.Fil: Palacio, Facundo Xavier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. División Zoología de Vertebrados. Sección Ornitología; Argentin

Understanding camera trade-offs through a Bayesian analysis of light field projections

Computer vision has traditionally focused on extracting structure,such as depth, from images acquired using thin-lens or pinhole optics. The development of computational imaging is broadening this scope; a variety of unconventional cameras do not directly capture a traditional image anymore, but instead require the joint reconstruction of structure and image information. For example, recent coded aperture designs have been optimized to facilitate the joint reconstruction of depth and intensity. The breadth of imaging designs requires new tools to understand the tradeoffs implied by different strategies.This paper introduces a unified framework for analyzing computational imagingapproaches. Each sensor element is modeled as an inner product over the 4D light field. The imaging task is then posed as Bayesian inference: given the observed noisy light field projections and a new prior on light field signals, estimatethe original light field. Under common imaging conditions, we compare the performance of various camera designs using 2D light field simulations. This framework allows us to better understand the tradeoffs of each camera type andanalyze their limitations

Massively-Parallel Feature Selection for Big Data

We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the challenges of Big Data FS PFBP partitions the data matrix both in terms of rows (samples, training examples) as well as columns (features). By employing the concepts of $p$-values of conditional independence tests and meta-analysis techniques PFBP manages to rely only on computations local to a partition while minimizing communication costs. Then, it employs powerful and safe (asymptotically sound) heuristics to make early, approximate decisions, such as Early Dropping of features from consideration in subsequent iterations, Early Stopping of consideration of features within the same iteration, or Early Return of the winner in each iteration. PFBP provides asymptotic guarantees of optimality for data distributions faithfully representable by a causal network (Bayesian network or maximal ancestral graph). Our empirical analysis confirms a super-linear speedup of the algorithm with increasing sample size, linear scalability with respect to the number of features and processing cores, while dominating other competitive algorithms in its class

Finding alternative right of way using multi-criteria decision analysis based on least cost path: A case study of 20’’Anoh pipeline

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesA multi-criteria decision analysis was conducted using a geographic information system (GIS) coupled with analytical hierarchy process (AHP) methods to evaluate and prioritize the pipeline project areas for Assa North Ohaji South. Alternative Right-of-Ways (ROWs) with two optimal routes were determined and compared with the existing ROWs. During the analysis, several criteria were considered to determine the least cost alternative ROW, including slope, geology, waterbodies, roads, land use, and land cover. The optimum route for connecting the source and destination was then determined. The LANDSAT 8 imageries of the study area were processed and classified into various land use and land cover types, which were then modeled using ArcMap 10.8 GIS software for routing analysis. It was used in the study to demonstrate the efficiency of MCDA LCP and AHP integration in generating optimum routes for the ANOH project. By avoiding steep slopes, built-up areas, and waterbodies, the optimal route avoided the limitations of the existing ROW. This route has a 22% reduction in length and will decrease construction costs, which is an indication of its efficiency