Artificial Intelligent Systems and Machine Learning

Sreenivasulu Dandagala ¹, M. V. Subba Reddy ¹, D. Srinivasa Murthy ¹, Gumageri Nagaraj ²

Affiliations
1 Department of Civil Engineering, Sree Vidyanikethan Engineering College, Sri Sainath Nagar, Rangampet-517102, IN
2 Department of Civil Engineering, Bearys Institute of Technology Mangalore, Innoli, Boliyar Village, Near Mangalore University, Mangalore, IN

Abstract

Reliable groundwater level forecasting is crucial and has become challenging task for the groundwater hydrologists. An accurate forecast plays a vital role for proper planning and utilization of groundwater resources in a sustainable manner. In the present study an attempt has been made to study one of the soft computing techniques such as Artificial Neural Networks (ANN) and its various applications in groundwater hydrology. Accurate prediction of Groundwater Level (GWL), assessment of water quality, concentrations of contamination, estimation of various aquifer properties and dynamic groundwater levels are the complex problems in the domain of groundwater hydrology. The ANN applications are studied in detailed manner and discussed its merits and demerits and given a brief discussion on its scope for future research work in the groundwater hydrology. Moreover, type of time series data, quality of data and other aspects would limit the applications of the modeling and requires further any model development. In the time series analysis, ANN has wide applications in the domain of civil engineering due to its capability of non-linear modelling in real world complex phenomena. ANN is non parametric method and prior knowledge is not mandatory. Above all these features makes ANN more attractive for time series modelling and forecasting. However, in the past a lot of successful applications have shown that ANN provide powerful tool for time series modeling. Therefore, based on the literature cited on ANN applications in the domain of groundwater hydrology, ANN can be suggested to be one of the effective tool for better GWL forecasting even with limited data.

Keywords

Groundwater Level, Artificial Neural Networks, Time Series.

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