The use of an artificial neural network (ANN) is becoming common due to its ability to analyse complex
nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the
nonlinear relationships between input and output data sets. This capability could efficiently be employed
for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in
nature and therefore, representing their physical characteristics is challenging. In this research, ANN
modelling is developed with the use of the MATLAB toolbox for predicting river stream flow coming
into the Ringlet reservoir in Cameron Highland, Malaysia. A back propagation algorithm is used to train
the ANN. The results indicate that the artificial neural network is a powerful tool in modelling rainfallrunoff.
The obtained results could help the water resource managers to operate the reservoir properly in
the case of extreme events such as flooding and drought.
Recently, license plate detection has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them work under restricted conditions such as fixed illumination, stationary background, and high resolution images. License plate detection plays an important role in car license plate recognition systems because it affects the accuracy and processing time of the system. This work aims to build a Car License Plate Detection (CLPD) system at a lower cost of its hardware devices and with less complexity of algorithms’ design, and then compare its performance with the local CAR Plate Extraction Technology (CARPET). As Malaysian plates have special design and they differ from other international plates, this work tries to compare two likely-design methods. The images are taken using a web camera for both the systems. One of the most important contributions in this paper is that the proposed CLPD method uses Vertical Edge Detection Algorithm (VEDA) to extract the vertical edges of plates. The proposed CLPD method can work to detect the region of car license plates. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 out of 643 images were successfully detected. Meanwhile, the average accuracy of locating car license plate was 90%. In this work, a comparison between CARPET and the proposed CLPD method for the same tested images was done in terms of detection rate and efficiency. The results indicated that the detection rate was 92% and 84% for the CLPD method and CARPET, respectively. The results also showed that the CLPD method could work using dark images to detect license plates, whereas CARPET had failed to do so.