950 resultados para Rain.


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Waterfalls attract tourists because they are aesthetically appealing landscape features that are not part of everyday experience. It is generally understood that falls are usually seen at their best when there is a copious flow of water, especially after heavy rain. Guidebooks often contain this observation when referring to waterfalls, sometimes warning readers that the flow may be severely reduced during dry periods. Indeed, many visitors are disappointed when they see falls at such times. Some are saddened when the discharge of a waterfall has been depleted by the abstraction of water upstream for power generation or other purposes. While, for those in search of the Sublime or merely the superlative, size is often important, small waterfalls can give great pleasure to lovers of landscape beauty. According to guidebooks, however, even these falls are usually best seen after rain. Drawing on tourist and travel literature and personal journals from the eighteenth century to the present, and with reference to examples from different parts of the world, this paper discusses the importance of discharge in the tourist experience of waterfalls.

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This paper studies the effect of rain on travel demand measured on the Tokyo Metropolitan Expressway (MEX). Rainfall data monitored by the Japan Meteorological Agency's meso-scale network of weather stations are used. This study found that travel demand decreases during rainy days and, in particular, larger reductions occur over the weekend. The effect of rainfall on the number of accidents recorded on 10 routes on the MEX is also analysed. Statistical testing shows that the average frequency of accidents, during periods of rainfall, is significantly different from the average frequency at other times.

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A number of instrumented laboratory-scale soil embankment slopes were subjected to artificial rainfall until they failed. The factor of safety of the slope based on real-time measurements of pore-water pressure (suction) and laboratory measured soil properties were calculated as the rainfall progressed. Based on the experiment measurements and slope stability analysis, it was observed that slope displacement measurements can be used to warn the slope failure more accurately. Further, moisture content/pore-water pressure measurements near the toe of the slope and the real-time factor of safety can also be used for prediction of rainfall-induced embankment failures with adequate accuracy.

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Poem

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Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide.

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Poem

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Rainfall can disrupt the balance of natural soil slope. This imbalance will be accelerated by existence of cracks in soil slope, which lead to decreasing shear strength and increasing hydraulic conductivity of the soil slope. Some research works have been conducted on the effects of surface-cracks on slope stability. However, the influence of deep-cracks is yet to be investigated. Limited availability of deep crack data due to the lack of effective sub-soil investigation methods could be one of the obstacles. To emphasize the effects of deep cracks in soil slope on its rain-induced instability, a natural soil slope in Indonesia that failed in 31st October 2010 due to heavy rainfall was analyzed for stability with and without deep cracks in the slope. The slope stability analysis was conducted using SLOPE/W coupling with the results of transient seepage analysis (SEEP/W) that simulate the pore-water pressure development in the slope during the rainfall. The results of Electrical Resistivity Tomography (ERT) survey, bore-hole tests and geometrical survey conducted on the slope before its failure were used to identify the soil layers’ stratification including deep cracks, the properties of different soil layers, and geometrical parameters of the slope for the analysis. The results showed that it is vital to consider the existence of deep crack in soil slopes in analysing their instability induced by rainfalls.

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Rainfall has been identified as one of the main causes for embankment failures in areas where high annual rainfall is experienced. The inclination of the embankment slope is important for its stability during rainfall. In this study, instrumented model embankments were subjected to artificial rainfalls to investigate the effects of the slope inclination on their stability. The results of the study suggested that when the slope inclination is greater than the friction angle of the soil, the failure is initiated by the loss of soil suction and when it is smaller than the friction angle of the soil, the failure is initiated by the positive pore water pressure developed at the toe of the slope. Further, slopes become more susceptible to sudden collapse during rainfall as the slope angle increases.

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Awareness to avoid losses and casualties due to rain-induced landslide is increasing in regions that routinely experience heavy rainfall. Improvements in early warning systems against rain-induced landslide such as prediction modelling using rainfall records, is urgently needed in vulnerable regions. The existing warning systems have been applied using stability chart development and real-time displacement measurement on slope surfaces. However, there are still some drawbacks such as: ignorance of rain-induced instability mechanism, mislead prediction due to the probabilistic prediction and short time for evacuation. In this research, a real-time predictive method was proposed to alleviate the drawbacks mentioned above. A case-study soil slope in Indonesia that failed in 2010 during rainfall was used to verify the proposed predictive method. Using the results from the field and laboratory characterizations, numerical analyses can be applied to develop a model of unsaturated residual soils slope with deep cracks and subject to rainwater infiltration. Real-time rainfall measurement in the slope and the prediction of future rainfall are needed. By coupling transient seepage and stability analysis, the variation of safety factor of the slope with time were provided as a basis to develop method for the real-time prediction of the rain-induced instability of slopes. This study shows the proposed prediction method has the potential to be used in an early warning system against landslide hazard, since the FOS value and the timing of the end-result of the prediction can be provided before the actual failure of the case study slope.

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Weather is one of the most significant elements affecting transit ridership on a daily basis. Until now, there has been limited focus in the literature investigating this issue. Adverse weather conditions impact travellers in choosing travel mode and route, travel schedule, and trip making itself. This paper explores the relationship between adverse weather and transit ridership by analysing the correlation between daily bus ridership and daily precipitation for a three-year period from 2010 to 2012. It is observed from the analysis that wet weather has varying impacts on daily bus ridership. Overall, rainfall negatively affects the daily bus ridership in this region. Morning peak-hours and weekend ridership were found more sensitive to rain than entire day’s ridership and weekdays. The study also found a negative correlation between the morning-peak precipitation level and the daily bus ridership, which suggests that a small amount of morning peak-hours rain reduces a significant amount bus ridership for the whole day. The analysis also confirms that summer rain has the most significant effect on ridership compared with the other three seasons. The study findings will contribute to enhancing the fundamental understanding of traveller behaviours, particularly mode choice behaviour under adverse weather conditions.

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Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain.

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Grateful Fateful Sunshine Rain is a permanent public artwork commissioned by Aria Property Group through a competitive process for the Austin apartment building in South Brisbane. Artist Statement: Residents of Brisbane have a complex relationship with weather. As the capital of the Sunshine State, weather is an integral part of the city’s cultural identity. Weather deeply affects the mood of the city – from the excitement of scantily clad partygoers on balmy December evenings and late February’s lethargy, to the deepening anxiety that emerges after 100 days of rain (or more commonly, 100 days without rain). With a brief nod to the city’s – now decommissioned – iconic MCL weather beacon, Grateful Fateful Sunshine Rain taps into this aspect of Brisbane’s psyche with poetic, illuminated visualisations of real-time weather forecasts issued by the Bureau of Meteorology. Each evening, the artwork downloads tomorrow’s forecast from the Bureau of Meteorology website. Data including, current local temperature, humidity, wind speed & direction, precipitation (rain, hail etc), are used to generate a lighting display that conveys how tomorrow will feel. The artwork’s background colour indicates the expected temperature – from cold blues through mild pastel pinks and blues to bright hot oranges and reds. White fluffy clouds roll across the artwork if cloud is predicted. The density of these clouds indicates the level of cover whilst movement indicates expected wind speed and direction. If rain is predicted, sparkles of white light will appear on top of whichever background colour is chosen for the next day’s temperature. Sparkles appear constantly before wet, drizzly days, and intermittently if scattered showers are predicted. Intermittent, but more intense sparkles appear before rain storms or thunderstorms. Research Contribution: The work has made contributions to the field in the way it rethinks approaches to the conceptualization, design and realization of illuminated urban media. This has led to new theorizations of urban media, which consider light and illumination can be used to convey meaningful data. The research has produced new methods for controlling illumination systems using tools and techniques typically employed in computation arts. It has also develop methods and processes for the design and production of illuminated urban media architectures that are connected to real time data sources, and do which not follow the assumed logics of screen based media and displays.

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We describe the development and parameterization of a grid-based model of African savanna vegetation processes. The model was developed with the objective of exploring elephant effects on the diversity of savanna species and structure, and in this formulation concentrates on the relative cover of grass and woody plants, the vertical structure of the woody plant community, and the distribution of these over space. Grid cells are linked by seed dispersal and fire, and environmental variability is included in the form of stochastic rainfall and fire events. The model was parameterized from an extensive review of the African savanna literature; when available, parameter values varied widely. The most plausible set of parameters produced long-term coexistence between woody plants and grass, with the tree-grass balance being more sensitive to changes in parameters influencing demographic processes and drought incidence and response, while less sensitive to fire regime. There was considerable diversity in the woody structure of savanna systems within the range of uncertainty in tree growth rate parameters. Thus, given the paucity of height growth data regarding woody plant species in southern African savannas, managers of natural areas should be cognizant of different tree species growth and damage response attributes when considering whether to act on perceived elephant threats to vegetation. © 2007 Springer Science+Business Media B.V.

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This thesis is concerned with the detection and prediction of rain in environmental recordings using different machine learning algorithms. The results obtained in this research will help ecologists to efficiently analyse environmental data and monitor biodiversity.