Application of artificial neural network in modelling of algal blooms an overview

Introduction Lakes have special importance owing to their value as natural ecosystems and centres of biodiversity, Role in sensing environmental pollution and value as a resource for water supply, hydropower, irrigation and amenity. Lakes are under increasing threat due to the nutrient enrichment from domestic and agricultural sources. Human have always been attached to lakes. Lakes are used by humans for many commercial purposes, including fishing, transportation, irrigation, industrial water supplies, and receiving waters for wastewater effluents. Lakes are the best available freshwater source on the Earth's surface. Aside from their importance for human use, lakes have intrinsic ecological and environmental values. With ecological modelling emerging as an invaluable tool for understanding the functioning of lakes, it is important to harness its potentials in ecosystem research. Though a number of models are available, the challenge is to identify the best possible model to be used for studying a lake ecosystem. Algal Blooms Aquatic ecosystems are very complex due to the diversity and connections of the components governing the system's dynamics. Explosion-like formations of algal blooms increasingly pollute both: salt and fresh water ecosystems throughout the world. Problems related to this nutrient over enrichment and excessive plant productions are probably the most common and have received public and scientific attention for the longest time. Owing to its negative impacts on human health and aquatic life, this widely reported phenomenon has become a serious environmental problem. This phenomenon results in numerous ecological and water quality changes in lakes. So it is essential to model the primary production in the lake or lagoon ecosystem. An Overview of Artificial Neural Networks Artificial Neural Networks (ANNs) have been widely used for modeling hydrological processes that are embedded with high non-linearity in both spatial and temporal scales. Further, ANNs are used as an effective approach for handling data in situations where the physical processes relationships are not fully understood and they are also well suited to modeling complex systems on a real-time basis. The ANNs are functionally equivalent to a nonlinear regression model. ANNs can identify and learn correlated patterns between input data sets and corresponding target values. After training, ANNs can be used to predict the output of new independent input data. ANNs imitate the learning process of the animal brain and can process problems involving very nonlinear and complex data even if the data are imprecise. Thus they are ideally suited for the modelling of ecological data which are known to be very complex and often non-linear [6, 7, 14]. ANNs can benefit more from large samples than linear statistical models can. It is interesting to note that ANNs do not necessarily require a larger sample than is required by linear models in order to perform well. Artificial neural networks (ANNs) are non-linear mapping structures based on the function of the human brain. They have been shown to be universal and highly flexible function approximators for any data. These make powerful tools for models, especially when the underlying data relationships are unknown. Artificial neural networks (ANNs) are among the newest signal-processing technologies and the field is highly