Data mining in real estate appraisal: a model tree and multivariate adaptive regression spline approach

In this paper we adopt two exploratory modelling techniques: Model Trees and Multivariate Adaptive Regression Splines. The objective is the building of two sale price prediction models in order to highlight possible market segments not detectable a priori. We show how these novel procedures can help to understand complex patterns and interactions among predictors in real estate appraisal

Multiple Autonomous Systems in Underwater Mine Countermeasures Mission Using Various Information Fusion as Navigation Aid

Autonomous bottom mine neutralization systems have a challenging task of mine reacquisition and navigation in the demanding underwater environment. Even after mine reacquisition, the neutralization payload has to be autonomously deployed near the mine, and before any action the verification (classification) of the existence of a mine has to be determined. The mine intervention vehicle can be an expendable (self-destroyed during the mine neutralization) or a vehicle that deploys the neutralization payload and it is retrieved at the end of the mission. Currently the systems developed by the research community are capable of remotely navigating a mine intervention underwater vehicle in the vicinity of the mine by using remote sonar aided navigation from a master vehicle. However, the task of successfully navigating the vehicle that carries the neutralization payload near the bottom and around the mine remains a challenge due to sea bottom clutter and the target signature interfering with the sonar detection. We seek a solution by introducing navigation via visual processing near the mine location. Using an onboard camera the relative distance to the mine-like object can be estimated. This will improve the overall vehicle navigation and rate of successful payload delivery close to the mine. The paper will present the current navigation system of the mine intervention underwater vehicle and the newly developed visual processing for relative position estimation

Assessment of the Geographic Distribution and Tools to Assist with Conservation of Spotted Turtles (Clemmys guttata) in West Virginia

Turtles are one of the most globally threatened vertebrate groups, largely due to habitat loss, commercial exploitation, climate change, disease, and invasive species. In the United States, the spotted turtle (Clemmys guttata) is among over 50 amphibian and reptile species recently petitioned to be listed under the Endangered Species Act of 1973 (ESA). The U.S. Fish and Wildlife Service subsequently determined the spotted turtle to be a candidate species for listing. Historically, spotted turtles were known to occur at a few locations in the eastern panhandle region of West Virginia. However, previous attempts to determine the distributional extent of spotted turtles within West Virginia did not use standardized site selection or field sampling methodologies. Furthermore, previous efforts did not benefit from using range wide distributional knowledge and high-resolution satellite imagery to guide survey site selection. Given the spotted turtle is currently a candidate species for listing under the ESA, there is a strong need to update our distributional knowledge for the species within West Virginia. The purpose of my thesis was to conduct field surveys to validate historically known spotted turtle localities and identify new populations, and to develop a wetland-level habitat suitability model to help guide future survey efforts. In addition, I developed a model that estimates depth of isolated wetlands in West Virginia to assist researchers and managers with classifying wetlands as potential habitat for wildlife species of concern. In Chapter 1, I provide background information on wetland water depth, methods of estimating depth, and its importance to spotted turtle habitat modeling. I also provide information on the biology and ecology of spotted turtles, and a brief summary of the known history of the species within West Virginia. Lastly, I state the goals of this thesis research and give a summary of chapter topics. In Chapter 2, I estimated mean monthly water depth across all mapped isolated wetlands in West Virginia to allow for a more refined classification of individual wetlands as potential habitat for focal wildlife species (e.g. spotted turtle). I found that watershed scale hydrological modeling is capable of providing reasonable estimates of water depth, indicating that it is possible to remotely estimate ecologically relevant wetland hydrological conditions across large extents. In Chapter 3, I summarize my field survey efforts to define the contemporary spotted turtle distribution in West Virginia , and provide details for a spotted turtle survey site rapid assessment form. I surveyed 62 sites across 41 unique wetlands within 10 counties in the eastern panhandle, north-central, and northern panhandle regions. Eighty unique spotted turtles were captured across 6 wetlands in 3 counties (Hampshire, Hardy, and Jefferson), all within the eastern panhandle. In October 2021, a photo of a hand captured spotted turtle near a surveyed site in Hardy county was sent to the West Virginia Division of Natural Resources, validating occupancy in Hardy County. There are currently a total of 7 known-occupied wetlands in the state. No spotted turtles were captured or encountered in the north-central or northern panhandle regions, suggesting that spotted turtle occupancy is restricted to the wider valleys of the Ridge and Valley and Blue Ridge physiographic ecoregions within West Virginia where habitat is present. In Chapter 4, I created wetland-level habitat suitability models using presence-absence data collected from surveys conducted in Chapter 3 and presence data provided by the Virginia Department of Wildlife Resources. I tested two statistical approaches, including a regression-based approach (i.e. logistic regression) and a decision tree-based approach (i.e. random forest). I found that the logistic regression model had very little predictive power and poor overall performance compared to the random forest model. I projected habitat suitability across 17,724 wetlands within the potential distribution of spotted turtles in West Virginia using the random forest model. The model classified 16,703 (94%) of wetlands as not suitable, 634 (4%) wetlands as low suitability, and 387 (2%) wetlands as high suitability. The random forest model indicated that areas of low topographic roughness and local topographic depressions with high wetland richness were positive indicators of spotted turtle habitat suitability. Furthermore, the model suggested that presence of non-suitable landcover types was important for the classification of suitable habitat, which agreed with our survey data, where we typically encountered spotted turtles in rural landscape surrounded by exurban development or agricultural lands

Geophysical Modeling of Typical Cavity Shapes to Calculate Detection Probability and Inform Survey Design

Feasibility analysis of near-surface cavity detection is presented using modeling of the gravity, gravity gradient, magnetic, magnetic gradient, and ground penetrating radar techniques. The geophysical signal is modeled over typical cavity shapes in three-dimensional subsurface environments with varying geologies and survey parameters. The cavity detection probability is calculated for each technique in the outlined environments and these values are used to aid technique choice, assess the feasibility of cavity detection, assess the limits of detection for each technique, and optimise survey design before entering the field. Tests in a range of conditions show that technique choice is conditional to site characteristics and site parameters, and highlight the need for modeling in the desk study stage of site investigation and survey design. Detection probability results show that standard survey direction practice in magnetometry is not always optimal, and demonstrate the importance of site specific noise level consideration. Comparisons with case study measurements demonstrate that the modelling and subsequent detection probability calculation chose appropriate techniques and survey parameters, but also highlights the limitations of the method

LINKING MULTIVARIATE OBSERVATIONS OF THE LAND SURFACE TO VEGETATION PROPERTIES AND ECOSYSTEM PROCESSES

Remotely sensed images from satellites and aircrafts, as well as regional networks and monitoring stations such as eddy flux towers, are collecting large volumes of multivariate data that contain information about the land surface and ecosystem processes. To derive from these systems information and knowledge relevant to how the Earth system functions and how it is changing, we need tools that to filter and mine the large data streams currently being acquired at different spatial and temporal scales. A challenge for Earth System Science lies in accurately identifying and portraying the relationships between the measurements at the sensor and quantity o f interest (i.e. ecosystem process or land surface property)

An integrated study of earth resources in the State of California using remote sensing techniques

The author has identified the following significant results. The supply, demand, and impact relationships of California's water resources as exemplified by the Feather River project and other aspects of the California Water Plan are discussed

Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering

Environmental shotgun sequencing (or metagenomics) is widely used to survey the communities of microbial organisms that live in many diverse ecosystems, such as the human body. Finding the protein-coding genes within the sequences is an important step for assessing the functional capacity of a metagenome. In this work, we developed a metagenomics gene prediction system Glimmer-MG that achieves significantly greater accuracy than previous systems via novel approaches to a number of important prediction subtasks. First, we introduce the use of phylogenetic classifications of the sequences to model parameterization. We also cluster the sequences, grouping together those that likely originated from the same organism. Analogous to iterative schemes that are useful for whole genomes, we retrain our models within each cluster on the initial gene predictions before making final predictions. Finally, we model both insertion/deletion and substitution sequencing errors using a different approach than previous software, allowing Glimmer-MG to change coding frame or pass through stop codons by predicting an error. In a comparison among multiple gene finding methods, Glimmer-MG makes the most sensitive and precise predictions on simulated and real metagenomes for all read lengths and error rates tested

A generic framework for context-dependent fusion with application to landmine detection.

For complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be a viable alternative to using a single classifier. Over the past few years, a variety of schemes have been proposed for combining multiple classifiers. Most of these were global as they assign a degree of worthiness to each classifier, that is averaged over the entire training data. This may not be the optimal way to combine the different experts since the behavior of each one may not be uniform over the different regions of the feature space. To overcome this issue, few local methods have been proposed in the last few years. Local fusion methods aim to adapt the classifiers\u27 worthiness to different regions of the feature space. First, they partition the input samples. Then, they identify the best classifier for each partition and designate it as the expert for that partition. Unfortunately, current local methods are either computationally expensive and/or perform these two tasks independently of each other. However, feature space partition and algorithm selection are not independent and their optimization should be simultaneous. In this dissertation, we introduce a new local fusion approach, called Context Extraction for Local Fusion (CELF). CELF was designed to adapt the fusion to different regions of the feature space. It takes advantage of the strength of the different experts and overcome their limitations. First, we describe the baseline CELF algorithm. We formulate a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. The context identification component thrives to partition the input feature space into different clusters (called contexts), while the fusion component thrives to learn the optimal fusion parameters within each cluster. Second, we propose several variations of CELF to deal with different applications scenario. In particular, we propose an extension that includes a feature discrimination component (CELF-FD). This version is advantageous when dealing with high dimensional feature spaces and/or when the number of features extracted by the individual algorithms varies significantly. CELF-CA is another extension of CELF that adds a regularization term to the objective function to introduce competition among the clusters and to find the optimal number of clusters in an unsupervised way. CELF-CA starts by partitioning the data into a large number of small clusters. As the algorithm progresses, adjacent clusters compete for data points, and clusters that lose the competition gradually become depleted and vanish. Third, we propose CELF-M that generalizes CELF to support multiple classes data sets. The baseline CELF and its extensions were formulated to use linear aggregation to combine the output of the different algorithms within each context. For some applications, this can be too restrictive and non-linear fusion may be needed. To address this potential drawback, we propose two other variations of CELF that use non-linear aggregation. The first one is based on Neural Networks (CELF-NN) and the second one is based on Fuzzy Integrals (CELF-FI). The latter one has the desirable property of assigning weights to subsets of classifiers to take into account the interaction between them. To test a new signature using CELF (or its variants), each algorithm would extract its set of features and assigns a confidence value. Then, the features are used to identify the best context, and the fusion parameters of this context are used to fuse the individual confidence values. For each variation of CELF, we formulate an objective function, derive the necessary conditions to optimize it, and construct an iterative algorithm. Then we use examples to illustrate the behavior of the algorithm, compare it to global fusion, and highlight its advantages. We apply our proposed fusion methods to the problem of landmine detection. We use data collected using Ground Penetration Radar (GPR) and Wideband Electro -Magnetic Induction (WEMI) sensors. We show that CELF (and its variants) can identify meaningful and coherent contexts (e.g. mines of same type, mines buried at the same site, etc.) and that different expert algorithms can be identified for the different contexts. In addition to the land mine detection application, we apply our approaches to semantic video indexing, image database categorization, and phoneme recognition. In all applications, we compare the performance of CELF with standard fusion methods, and show that our approach outperforms all these methods