MODELING OF SEASONAL VARIATION OF GROUNDWATER LEVELS USING ARTIFICIAL NEURAL NETWORK - A CASE STUDY (APO/GUDU) METROPOLIS, ABUJA

📄 Project Abstract

Artificial Neural Networks (ANNs) have been successfully used for predicting and modeling groundwater levels for a good period. In this paper, I use Artificial Neural Network (ANN) and Multi-Linear Regression (MLR) to model groundwater levels in Abuja, Nigeria using Apo-Gudu as a case study. After setting the models with the various parameters that affect groundwater level in Abuja, the developed ANN and MLR should be able to produce near accurate predictions of the groundwater level variations using just the input variables that have been identified as maximum and minimum temperature, rainfall, relative humidity, and wind speed. To accomplish this, the models are first aligned on a preparation dataset to perform as they should as neural networks to forecasts future groundwater levels utilizing past noticed groundwater levels and outside inputs. Reproductions are then delivered on another informational index by iteratively taking care of back the anticipated ground-water levels, alongs...