Development of generalized feed forward network for predicting annual flood (depth) of a tropical river

The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and Root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable

Assessing the impact of climate change on the built environment in Kaduna Metropolis and environs

The Kaduna's tropical climate for the last twenty years with its uniform characteristics of high temperature, humidity and precipitation witnessed an unprecedented widespread weather variability resulting to an extended dry spell and rising temperature. The study was conducted in Kaduna metropolis and environs using integrated dynamic model technique with the aim to ascertain the impact of climate change on the environment. Data were compiled from various land use map and historical climate and weather data (rainfall, ambient air temperature, heat weave, wind speed, direction, cloud cover and relative humidity) from the Country Planning department and Nigeria Meteorological Department respectively. The finding indicated that higher temperatures intensified urban heat island, especially during dry season (February to April). Decades rainfall revealed an upward trend of 408mm while temperature shows to increase by 0.1996oC. The average evaporation was found to be 163 cm yr−1 (±9%) and likewise relative humidity was found to increase by 66.5%. The rain fall regime in the metropolis is highly variable and its seasonality change is another good indicator of climate change which revealed some fluctuation in rainfall seasonality in the metropolis resulting to flooding. Also, the monthly evaporations and relative humidity have seasonal variability indicating an important relationship between evaporation, relative humidity and seasonal changes in the environment. Conclusively, there is no doubt that the human populations, infrastructure and ecology of cities are at risk from the impacts of climate change as flooding is more frequent and intense rainfalls leading to stream and riverine flooding and overwhelming of urban drainage systems. However, tools are becoming available for addressing some of the worst effects. For example, appropriate building design and climate sensitive planning, avoidance of high-risk areas through more stringent development control, incorporation of climate change allowances in engineering standards applied to flood defences