Hybridization-Of-ANN-with-metaheuristic-Algorithm-for-predicting-groundwater-quality

Groundwater contamination with arsenic is a serious problem in many parts of the world, and can have severe health consequences for those who consume it. In this project, we aim to predict the arsenic content in groundwater using artificial neural networks (ANN), specifically backpropagation neural network (BPNN), and Whale Optimization Algorithm (WOA).The project involves collecting data on arsenic levels in groundwater from various locations, along with information on environmental factors that may affect the arsenic content. The data is then preprocessed to clean and transform it, and split into training and testing datasets.We use BPNN and WOA to build prediction models based on the training dataset. BPNN is a commonly used neural network model for regression and classification tasks, while WOA is a nature-inspired optimization algorithm that can be used to optimize the weights and biases of the neural network.The performance of the BPNN and WOA models is then evaluated using the testing dataset, and compared against each other to determine which method yields better results. We also evaluate the impact of different input variables on the prediction accuracy of the models.The results of this project can have important implications for water management and public health, as accurate prediction of arsenic levels in groundwater can help prevent exposure to this toxic element. Furthermore, the use of advanced machine learning techniques like ANN and WOA can provide insights into the complex relationships between arsenic content and environmental factors, and may lead to the development of more effective strategies for managing groundwater resources.