Fluvial particle characterization using artificial neural network and spectral image processing

Sand, chemical waste, microbes and other solid materials flowing with the water bodies are of great significance to us as they cause substantial impact to different sectors including drinking water management, hydropower generation, irrigation, aquatic life preservation and various other socio-ecological factors. Such particles can't completely be avoided due to the high cost of construction and maintenance of the waste-treatment methods. A detailed understanding of solid particles in surface water system can have benefit in effective, economic, environmental and social management of water resources. This paper describes an automated system of fluvial particle characterization based on spectral image processing that lead to the development of devices for monitoring flowing particles in river. Previous research in coherent field has shown that it is possible to automatically classify shapes and sizes of solid particles ranging from 300-400 μm using artificial neural networks (ANN) and image processing. Computer facilitated with hyper spectral and multi spectral images using ANN can further classify fluvial materials into organic, inorganic, biodegradable, bio non degradable and microbes. This makes the method attractive for real time monitoring of particles, sand and microorganism in water bodies at strategic locations. Continuous monitoring can be used to determine the effect of socio-economic activities in upstream rivers, or to monitor solid waste disposal from treatment plants and industries or to monitor erosive characteristic of sand and its contribution to degradation of efficiency of hydropower plant or to identify microorganism, calculate their population and study the impact of their presence. Such system can also be used to characterize fluvial particles for planning effective utilization of water resources in micro-mega hydropower plant, irrigation, aquatic life preservation etc.