Predicting aquatic ecosystem health using machine learning algorithms

The most instinctive natural resource on the planet is water. Hydrological researchers must take the vital step of predicting water level and taking the necessary action to prevent the impending water crisis in order to avoid the shortage of water. Several hydrological research have demonstrated the potential of Artificial Neural Networks (ANN) as a new technology for anticipating groundwater levels. The extended feature and flawed data set for prediction result in an unpredictable and inconsistent aspect. To prevent a water crisis, strict groundwater management measures must be implemented. Artificial Neural Networks (ANN) are effective in three areas, according to recent research: they can handle very huge data sets, complex computing challenges, and training at the discrete level of representation or depiction. Strong and fast hardware is desired for deep learning stimulation. A single-core CPU will not be able to create reliable predictions with a flawed and inaccurate data set of input variables; on the other hand, a multi-core CPU will make predictions more efficiently. A multi-processor system with thousands of computer units called cores is called a Graphical Processing Unit (GPU).