EVALUATING CLIMATE CHANGE MITIGATION AND ADAPTATION POLICIES ON THE U.S. 50 STATES’ HAZARD MITIGATION PLANS

Climate change brings uncertain risks of climate-related natural hazards. The U.S. Federal Emergency Management Agency (FEMA 2011) has issued a policy directive to integrate climate change adaptation actions into hazard mitigation programs, policies, and plans. However, to date there has been no comprehensive empirical study to examine the extent to which climate change issues are integrated into State Hazard Mitigation Plans (SHMPs). This study develops 18 indicators to examine the extent of climate change considerations in the 50 SHMPs. The results demonstrate that these SHMPs treat climate change issues in an uneven fashion, with large variations present among the 50 states. The overall plan quality for climate change considerations was sustained at an intermediate level with regard to climate change-related awareness, analysis, and actions. The findings confirm that climate change concepts and historical extreme events have been well recognized by the majority of SHMPs. Even though they are not specific to climate change, mitigation and adaptation strategies that can help reduce climate change risks have been adopted in these plans. However, the plans still lack a detailed assessment of climate change and more incentives for collaboration strategies beyond working with emergency management agencies. Advisor: Zhenghong Tan

Application of statistical learning theory to plankton image analysis

Submitted to the Joint Program in Applied Ocean Science and Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy At the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2006A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder (VPR). The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system includes four major components: pattern representation/feature measurement, feature extraction/selection, classification, and abundance estimation. After an extensive study on a traditional learning vector quantization (LVQ) neural network (NN) classifier built on shape-based features and different pattern representation methods, I developed a classification system combined multi-scale cooccurrence matrices feature with support vector machine classifier. This new method outperforms the traditional shape-based-NN classifier method by 12% in classification accuracy. Subsequent plankton abundance estimates are improved in the regions of low relative abundance by more than 50%. Both the NN and SVM classifiers have no rejection metrics. In this thesis, two rejection metrics were developed. One was based on the Euclidean distance in the feature space for NN classifier. The other used dual classifier (NN and SVM) voting as output. Using the dual-classification method alone yields almost as good abundance estimation as human labeling on a test-bed of real world data. However, the distance rejection metric for NN classifier might be more useful when the training samples are not “good” ie, representative of the field data. In summary, this thesis advances the current state-of-the-art plankton recognition system by demonstrating multi-scale texture-based features are more suitable for classifying field-collected images. The system was verified on a very large realworld dataset in systematic way for the first time. The accomplishments include developing a multi-scale occurrence matrices and support vector machine system, a dual-classification system, automatic correction in abundance estimation, and ability to get accurate abundance estimation from real-time automatic classification. The methods developed are generic and are likely to work on range of other image classification applications.This work was supported by National Science Foundation Grants OCE-9820099 and Woods Hole Oceanographic Institution academic program

Global existence and propagation speed for a generalized Camassa-Holm model with both dissipation and dispersion

In this paper, we study a generalized Camassa–Holm (gCH) model with both dissipation and dispersion, which has (N+1)-order nonlinearities and includes the following three integrable equations: the Camassa–Holm, the Degasperis–Procesi, and the Novikov equations, as its reductions. We first present the local well-posedness and a precise blow-up scenario of the Cauchy problem for the gCH equation. Then, we provide several sufficient conditions that guarantee the global existence of the strong solutions to the gCH equation. Finally, we investigate the propagation speed for the gCH equation when the initial data are compactly supported