Toluwaleke Ajayi^1,, Dina L.Lopez¹, Abiodun E.Ayo-Bali²

In southeast Ohio, Raccoon Creek Watershed (RC) has an extensive mining history resulting in acid mine drainage (AMD) and subsequent environmental problems. Modeling of the discharge and chemistry of AMD impacted Hewett Fork, a tributary of Raccoon Creek, is the focus of this paper. Discharge measurements are collected by the United States Geological Survey (USGS) gage station at the Bolin Mills (BM) station on the main stem of RC. This data for the period 2011-2019 has been analyzed to develop a prediction model for BM discharge and subsequently use the model to predict flow and water chemistry in Hewett Fork (HF). The Neural Network Model using group method of data handling (GMDH) and generalized regression neural network (GRNN) shows a variation of prediction models for BM due to parameters such as decay factor in API and ATI, as well as in the evapotranspiration input variables of the model. However, the study reveals that the GRNN model for BM is the most suitable for BM prediction based on its performance, with an r-value greater than 0.90, and its ease in predicting discharge by specifying a data set to be added to the data set for training and calibrating the network. The result of the water chemistry model using GMDH, with r values greater than 0.80 for each model, shows the input variables have a good capacity to predict chemical concentration/load in the HF stream. This study shows that Artificial Neural Network (ANN) modeling can help to model successfully and predict flow and chemical evolution of rivers in Ohio and other parts of the world.

watershed, modeling, artificial neural network, discharge, prediction, hydrology