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Flood is a natural event that cause varying degree of losses despite initiatives to mitigate its impact. As the drive to retore normalcy in flood riddle areas increases, concerns about the vulnerability of areas in relation to households’ responses to flood has emerged. This study therefore, identified areas vulnerable to flood; elicit households’ sociodemographic attributes and examine their level of preparedness to flood in Ogunpa and Oni River Basin. A GIS-based approach was adopted along with 121 well-structured questionnaires administered to the respondents. A multi-criteria analysis (that considers slope, rainfall, soil type, DEM and NDVI was adopted), descriptive statistic and binary logistic model was used to achieve the objectives. The results from the vulnerability map revealed varying vulnerability status across Ogunpa and Oni River Basin. In addition, the sociodemographic statistics revealed important variables that influences household decision to prepare for flood. It was also known that factors that drive households to prepare for flood vary across households in Ogunpa and Oni River Basin. The study recommends enlightenment campaign on proper refuse disposal, strengthening of environmental regulatory agency, adoption of participatory approach in the buildup of environmental policy and increasing the level of awareness on the need for insurance policy that covers household against disaster such as flood.

The importance of health to the overall wellbeing of every nation cannot be overemphasized. Therefore, there is a need for effective planning and management of the healthcare facilities in order to achieve optimum result in the country for equitable distributi on of health facilities as a factor for sustaining the population in the cities. This research explores potential use of Goespatial Techniques for analysing public healthcare facilities accessibility and distribution pattern in Ile - Ife metropolis. Primary and secondary data were acquired. Primary data include questionnaire administration and Global Positioning System (GPS) receiver’s coordinate points of the health facilities while secondary data include administrative map of Ile - Ife metropolis and geo - eye satellite image 2011. These set of data were integrated into Arcgis environment and the following spatial analyses were carried out; overlay

Accurate prediction and determination of the effective earth radius factor (k-factor) is critical for optimal performance in the design and planning of terrestrial line of sight communication links. In this work an artificial neural network (ANN) model is developed and used to predict k-factor values for four towns in Ondo State using satellite derived data. The towns are Okitipupa (6.5°N, 4.78°E), Ondo (7.11°N, 4.83°E), Akure (7.25°N, 5.2°E) and Ikare (7.52°N, 5.75°E). A feed forward back propagation ANN was implemented, thereafter trained, validated and tested using satellite derived data for the period from 1984 to 2002. Mean absolute error (MAE) was used to evaluate the performance of the ANN. The MAE values obtained were 0.0024, 0.0014, 0.002 and 0.003 for Okitipupa, Akure, Ikare and Ondo towns respectively. Contour map showing the predicted k-factor values interpolated over the map of Ondo state was plotted using Geographical Information System (GIS) techniques. The study concluded that ANN presents an effective means of predicting the average k-factor values over a geographical location.