Thesis--School of Advanced Airpower Studies, 1996. / Shipping list no.: 98-0921-M. "May 1997." Includes bibliographical references (p. 27-36). Also available via Internet from the Air University Press web site. Address as of 10/10/03: http://aupress.au.af.mil/SAAS%5FTheses/Coble/coble.pdf; current access is available via PURL.

Thesis--School of Advanced Airpower Studies, 1996. / "May 1997." Title from Internet title screen. Includes bibliographical references (p. 27-36).

Thesis (M.S.)--University of Wisconsin--Madison, 1966. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 43-44.

Traditional methods such as distance weighing, correlation and data driven methods have been used in the estimation of missing precipitation data. Also common is the use of radar (NEXRAD) data to provide better spatial distribution of precipitation as well as infilling missing rain gage data. Conventional regression models are often used to capture highly variant nonlinear spatial and temporal relationships between NEXRAD and rain gage data. This study aims to understand and model the relationships between radar (NEXRAD) estimated rainfall data and the data measured by conventional rain gages. The study is also an investigation into the use of emerging computational data modeling (inductive) techniques and mathematical programming formulations to develop new optimal functional approximations. Radar based rainfall data and rain gage data are analyzed to understand the spatio-temporal associations, as well as the effect of changes in the length or availability of data on the models. The upper and lower Kissimmee basins of south Florida form the test-bed to evaluate the proposed and developed approaches and also to check the validity and operational applicability of these functional relationships among NEXRAD and rain gage data for infilling of missing data. / by Delroy Peters. / Thesis (M.S.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.

Radar rainfall estimates have become a decision making tool for scientists, engineers and water managers in their tasks for developing hydrologic models, water supply planning, restoration of ecosystems, and flood control. In the present study, the utility of a power function for linking the rain gauge and radar estimates has been assessed. Mean daily rainfall data from 163 rain gauges installed within the South Florida Water Management District network have been used and their records from January 1st, 2002 to October 31st, 2007 analyzed. Results indicate that the power function coefficients and exponents obtained by using a non-linear optimization formulation, show spatial variability mostly affected by type of rainfall events occurring in the dry or wet seasons, and that the linear distance from the radar location to the rain gauge has a significant effect on the computed values of the coefficients and exponents. / by Mario Mayes-Fernandez. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

In this thesis the two disciplines of Statistics and Artificial Neural Networks are combined into an integrated study of a data set of a weather modification Experiment. An extensive literature study on artificial neural network methodology has revealed the strongly interdisciplinary nature of the research and the applications in this field. An artificial neural networks are becoming increasingly popular with data analysts, statisticians are becoming more involved in the field. A recursive algoritlun is developed to optimize the number of hidden nodes in a feedforward artificial neural network to demonstrate how existing statistical techniques such as nonlinear regression and the likelihood-ratio test can be applied in innovative ways to develop and refine neural network methodology. This pruning algorithm is an original contribution to the field of artificial neural network methodology that simplifies the process of architecture selection, thereby reducing the number of training sessions that is needed to find a model that fits the data adequately. [n addition, a statistical model to classify weather modification data is developed using both a feedforward multilayer perceptron artificial neural network and a discriminant analysis. The two models are compared and the effectiveness of applying an artificial neural network model to a relatively small data set assessed. The formulation of the problem, the approach that has been followed to solve it and the novel modelling application all combine to make an original contribution to the interdisciplinary fields of Statistics and Artificial Neural Networks as well as to the discipline of meteorology. / Mathematical Sciences / D. Phil. (Statistics)

In this thesis the two disciplines of Statistics and Artificial Neural Networks are combined into an integrated study of a data set of a weather modification Experiment. An extensive literature study on artificial neural network methodology has revealed the strongly interdisciplinary nature of the research and the applications in this field. An artificial neural networks are becoming increasingly popular with data analysts, statisticians are becoming more involved in the field. A recursive algoritlun is developed to optimize the number of hidden nodes in a feedforward artificial neural network to demonstrate how existing statistical techniques such as nonlinear regression and the likelihood-ratio test can be applied in innovative ways to develop and refine neural network methodology. This pruning algorithm is an original contribution to the field of artificial neural network methodology that simplifies the process of architecture selection, thereby reducing the number of training sessions that is needed to find a model that fits the data adequately. [n addition, a statistical model to classify weather modification data is developed using both a feedforward multilayer perceptron artificial neural network and a discriminant analysis. The two models are compared and the effectiveness of applying an artificial neural network model to a relatively small data set assessed. The formulation of the problem, the approach that has been followed to solve it and the novel modelling application all combine to make an original contribution to the interdisciplinary fields of Statistics and Artificial Neural Networks as well as to the discipline of meteorology. / Mathematical Sciences / D. Phil. (Statistics)

PhDRDV / Institute for Rural Development / Currently, there is limited understanding, appreciation and dissemination of indigenous raining making practices. Yet indigenous rain making is part of the rich African heritage. The current study was premised on the view that indigenous rain making practices can help combat the negative effects of climate change if properly integrated with western science. A mixture of exploratory and survey designs was adopted in this study, which sought to examine the common indigenous rainmaking practices in Chimanimani District of Zimbabwe prior to developing strategies for reducing the negative impacts of climate change on the livelihoods of rural households. Various studies with the following specific objectives were carried out: to analyze the general community perceptions on the potential of indigenous rain making practices in combating the negative effects of climate change; to examine the components of indigenous rainmaking practices; analyse the means of disseminating knowledge on indigenous rainmaking; to identify the negative effects of climate change on the livelihoods of rural households; to assess the effectiveness of existing strategies used by households to cope with the negative effects of climate change; and to propose strategies for utilizing indigenous rainmaking practices to counter the negative effects of climate change on the livelihoods of rural households. Semi-structured interview guides and a questionnaire requiring responses on a Likert-type scale were used to collect data. Key informants and ordinary community members were selected using judgmental, convenient and snowballing sampling techniques. The Thematic Content Analysis technique was used to draw meaning out of the qualitative data. Chi-Square tests for Goodness of Fit were conducted using the Statistical Package for Social Sciences (SPSS) to establish if there were significant relationships among perceptions. It was indicated that the shift in seasons as exemplified by the Nyamavhuvhu wind which now swept Chimanimani in September or October instead of end of July to August was evidence of climate change. Responses with respect to the negative effects of climate change included food insecurity, and drying up of streams and rivers. Availability of water for domestic, agricultural and animal use was becoming increasingly unreliable. The respondents argued that they believed in the effectiveness of indigenous rain making if it is conducted following local customs and traditions. Significant differences in the following perceptions were observed for “Besides makoto and Christian prayers there are other common rainmaking practices practiced in Chimanimani District” (p < 0.05). Similar results were observed with regard to “I believe indigenous and western knowledge of rainmaking can complement each other” (P < 0.001), and “There is increase in pests and plant diseases than before” (P < 0.01). Components of indigenous rain making v identified in the current study included rain making ceremonies (makoto), which entailed use of beer, sacrificial bird (normally a cock) and natural resources conservation such as keeping places for local rain making rituals sacred (zvitenguro), not destroying very big trees for example fig tree (muonde: Ficus capensis), mukute (Syzygium cordatum) and others, and treating forests as sacred. With respect to the negative effects of climate change, a highly significant difference was observed for duration of stay in relation to, “There is now a high risk in planting winter wheat due to changes in climate” (P < 0.01); “Wetlands are disappearing in our area” (P < 0.01); “There is general reduction in yields due to climate change” (P < 0.001) and “We are experiencing scarcity of water for domestic animals and for household use” (P < 0.05). Lastly, highly significant relationships between “Rivers are drying up in our area” and education (P < 0.01) and duration of stay (P < 0.001). Methods used to disseminate indigenous knowledge of rain making were said to be ineffective. Information was being passed on through oral means. It was indicated that better use of modern technology and social media, in particular radio, television, Twitter, WhatsApp and Facebook might enhance people’s knowledge on indigenous rain making. By so doing, the perception that indigenous rain making was merely history and not knowledge that can be used in people’s daily lives would be eliminated. Furthermore, current strategies utilized to combat the negative effects of climate change were reported to be unsustainable. Among these were reliance on harvesting wild fruits for sale and hunting. Human activities such as veld fires, deforestation and over harvesting of wildlife were viewed in negative light with respect to combating negative effects of climate change. It was proposed that communities should revert to respecting traditional beliefs of conserving forests. This said to be key in normalizing climate, attracting back the birds and animals that used to be key in weather forecasting. Replanting and indiscriminate cutting of trees along rivers as effective prevention of stream bank cultivation were proposed. Re-introduction of heavy fines by traditional leadership was suggested as a tried and tested strategy that was no longer being applied when implementing conservation initiatives. The observation made in this study that western science and indigenous rain making practices were similar in many respects, suggested that these were opportunities that could be used to anchor strategies for integrating them. In addition to this, the need for establishing collective deliberation or interface platforms coupled with continuous communication and careful management of intellectual property was obvious.