Identification of pollution sources using artificial neural network (ANN) and multilevel breakthrough curve (BTC) characterization

Abstract

Abstract A pollution source in groundwater may be active at some location for certain periods. There may be multiple potential sources responsible for observed contamination at observation wells. The contamination witnessed in observation wells at different times establishes breakthrough curves (BTCs). These BTCs are usually employed for source identification. In this work, single and multistage artificial neural network (ANN) is employed to identify the potential pollution sources. Temporally varying potential pollution sources are generated using uniform random numbers. These source fluxes are further applied to the simulation of the pollution concentration at observation wells. BTC at an observation well is characterized by statistical parameters and data mining. Characterized BTCs are inputs and source fluxes are outputs of ANN models. Initial stage ANN models are developed at the specified observation well locations, using multilevel BTC characterization. These initial meta models are utilized for the development of intermediate models. Further, the intermediate models are employed for final stage identification. These multi-stage ANN models are found to perform comparatively better than single stage ANN models.