Jamaica, the third largest island in the Caribbean, has been affected significantly by flooding and flood-related damage. Hence assessing the probability of flooding and susceptibility of a place to flood hazard has become a vital part of planning and development. In addition to heavy rainfall from tropical storms and Atlantic hurricanes, several terrestrial factors play significant roles in flooding, including local geology, geomorphology, hydrology and land-use. In this study, a GIS-based multi-criteria statistical methodology was developed to quantify hazard potential and to map flood characteristics. Fourteen factors potentially responsible for flooding were identified and used as initial input in a hybrid model that combined principal component analysis with logistic regression and frequency distribution analysis. Of these factors, seven explained 65 % of the variation in the data: elevation, slope angle, slope aspect, flow accumulation, a topographic wetness index, proximity to a stream network, and hydro-stratigraphic units. These were used to prepare the island’s first map of flood hazard potential. Hazard potential was classified from very low to very high, nearly one-fifth (19.4 %) of the island was included within high or very high flood hazard zones. Further analysis revealed that areas prone to flooding are often low-lying and flat, or have shallow north- or northwest-facing slopes, are in close proximity to the stream network, and are situated on underlying impermeable lithology. The multi-criteria hybrid approach developed could classify 86.8 % of flood events correctly and produced a satisfactory validation result based on the receiver operating characteristic curve. The statistical method can be easily repeated and refined upon the availability of additional or higher quality data such as a high resolution digital elevation model. Additionally, the approach used in this study can be adopted to evaluate flood hazard in countries with similar characteristics, landscapes and climatic conditions, such as other Caribbean or Pacific Small Island Developing States.

Noise is a pervasive workplace hazard that varies spatially and temporally. Hazard mapping is a useful way to communicate intensity and distribution of noise sources in the workplace. These maps can be created using a stationary network of sensors, although the cost of noise measurement instruments has prohibited their use in such a network. The objectives for this work were to (1) develop an inexpensive noise sensor (<$100) that measures A-weighted sound pressure levels within ±2 dBA of a Type 2 sound level meter (SLM, ~$1,800); and (2) evaluate 50 noise sensors before field deployment as part of an inexpensive sensor network. The inexpensive noise sensor consists of an electret condenser microphone, an amplifier circuit, and a microcontroller with a small form factor (28mm by 47 mm by 9 mm) than can be operated as a stand-alone unit. Laboratory tests were conducted to evaluate 50 of the new sensors at 5 test levels. The testing levels were (1) ambient noise in a quiet office, (2) a pink noise test signal from 65 to 85 dBA in 10 dBA increments, and, (3) 94 dBA using a SLM calibrator. The difference between the output of the sensor and SLM were computed for each level and overall. Ninety-four percent of the noise sensors (n=46) were within ± 2 dBA of the SLM for noise levels from 65 dBA to 94 dBA. As noise level increased, bias decreased, ranging from 18.3% in the quiet office to 0.48% at 94 dBA. Overall bias of the sensors was 0.83% across the 75 dBA to 94 dBA range. These sensors are available for a variety of uses and can be customized for many applications, including incorporation into a stationary sensor network for continuously monitoring noise in manufacturing environments.

The distribution of landslide type and age was analyzed to determine the causes and timing of landsliding, and to assess landslide hazards in the study area. 1173 landslides and zones of landsliding were mapped on 1:15,840 scale air photos and designated by their style of movement and age. Slides were assigned to one of four age classes based on their degree of m orphologic modification visible on air photos. Relative dating (RD) methods previously applied to glacial deposits were used to refine and calibrate the age classification. Eleven RD para meters were measured on 21 rockslide and 19 glacial deposits. Cluster analyses were run on the RD data set. Slides assigned to Age-Classes 4, 3+, and 2 tend to cluster with probable Pinedale, early Holocene, and Neoglacial-age moraines respectively. Cluster analyses indicate poor age resolution by the RD method from approximately early Altithermal to early Neoglacial time. Landslide age cannot be resolved in this study to a finer degree by the RD method than by the morphologic (air -photo) method. However, cluster analyses generally confirm age assignments and absolute age estimates of the four landslide age classes, despite limitations of the RD method such as boulder spalling, and variations in lithology, deposit type, and elevation/climate between sampled deposits. The temporal distribution of landslides indicates that mass movements may have occurred rather uniformly throughout Holocene time, with slightly higher rates of sliding during post-Altithermal time due to climatic effects associated with Neoglacial advances. Spatial analyses indicate that landslides cover 73% of the Cretaceous section. Development, such as logging and road construction, could trigger landsliding in the Cretaceous section. Landslides account for 15% and 10% of the outcrop areas of the Paleozoic and Triassic-Jurassic sections respectively. Debris flows and slump-earth flows dominate sliding in both sections, with minor numbers of rockslides present. Debris flows pose the greatest hazard in both sections. Fine-grained stratigraphic units have the highest landslide densities in both sections. The previous event locations define areas most susceptible to future sliding.

Neste trabalho é proposta a modelagem de uma ferramenta de auxílio à tomada de decisão com base na avaliação do nível de perigo de colisão entre duas aeronaves em operação de aproximação em pistas de aterrissagem paralelas (UCSPA - Ultra Closely Spaced Parallel Approaches). A ferramenta computacional utilizada na simulação e na obtenção dos dados numéricos está fundamentada no Método de Monte Carlo. / In this work is proposed an aid tool modeling for decision-making process based on collision hazard evaluation between two aircrafts on UCSPA (Ultra Closely Spaced Parallel Approaches) scenario. The computation tool used in the simulation to obtain the numerical data is based on Monte Carlo Method.

Neste trabalho é proposta a modelagem de uma ferramenta de auxílio à tomada de decisão com base na avaliação do nível de perigo de colisão entre duas aeronaves em operação de aproximação em pistas de aterrissagem paralelas (UCSPA - Ultra Closely Spaced Parallel Approaches). A ferramenta computacional utilizada na simulação e na obtenção dos dados numéricos está fundamentada no Método de Monte Carlo. / In this work is proposed an aid tool modeling for decision-making process based on collision hazard evaluation between two aircrafts on UCSPA (Ultra Closely Spaced Parallel Approaches) scenario. The computation tool used in the simulation to obtain the numerical data is based on Monte Carlo Method.