Particles flowing along with water largely affect safe drinking water, irrigation, aquatic life preservation and hydropower
generation. This research describes activities that lead to development of fluvial particle characterization that includes
detection of biological and non-biological particles and shape characterization using Image Processing and Artificial
Neural Network (ANN).
Fluvial particles are characterized based on multi spectral images processing using ANN. Images of wavelength of
630nm and 670nm are taken as most distinctive characterizing properties of biological and non-biological particles found
in Bagmati River of Nepal. The samples were collected at pre-monsoon, monsoon and post-monsoon seasons. Random
samples were selected and multi spectral images are processed using MATLAB 6.5. Thirty matrices were built from
each sample. The obtained data of 42 rows and 60columns were taken as input training with an output matrix of 42 rows
and 2 columns. Neural Network of Perceptron model was created using a transfer function. The system was first
validated and later on tested at 18 different strategic locations of Bagmati River of Kathmandu Valley, Nepal. This
network classified biological and non biological particles.
Development of new non-destructive technique to characterize biological and non-biological particles from fluvial
sample in a real time has a significance breakthrough. This applied research method and outcome is an attractive model
for real time monitoring of particles and has many applications that can throw a significant outlet to many researches and
for effective utilization of water resources. It opened a new horizon of opportunities for basic and applied research at
Kathmandu University in Nepal.