Ecological models can be used to predict or forecast a system's ecological response (e.g., chlorophyll concentration) to various system states (e.g., nutrient concentrations, water temperature, wind speed), to model the underlying physical processes as accurately as possible in order to gain a better understanding of the physical system being modeled or to assist decision-makers with choosing between a number of management options (e.g., different flow management strategies for controlling algal blooms). The philosophy that underpins the development of ecological models can also vary, and generally belongs to one of two broad categories: process-driven (e.g., equations of growth and decay) and data-driven (e.g., regression, time series, and artificial neural network models). However, regardless of purpose and type, all ecological models represent a functional relationship (f(.)) between the desired model output (y) and one or more model inputs (X):
where Y is a vector of model parameters and " is the model error, which is usually white noise.
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Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.