Faculty Publications
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Item Fuzzy logic modeling for groundwater level forecasting of west coast region in India(2011) Dandagala, D.; Deka, P.C.Forecasting the groundwater table in unconfined aquifer is essential for efficient planning of conjunctive use in a basin. In this study, fuzzy logic (FL) models have been developed for groundwater level forecasting in west coast humid region of Karnataka state, India. The FL modeling was carried out to forecast the groundwater table by one week lead time at three different sites over the study area. Mamdani fuzzy inference system was adopted in the present study and finally centroid of area defuzzification method has been applied to obtain crisp output. The results concluded that the FL model performed quite satisfactorily as assessed by various performance indices such as Root mean square error, Coefficient of correlation, and Mean absolute error. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item A comparative study on RBF and NARX based methods for forecasting of groundwater level(2011) Dandagala, D.; Deka, P.C.Evaluation and forecasting of groundwater levels through time series model (s) helps for the sustainable development of groundwater resources. The focus of the present study is on the application of Radial Basis Function (RBF) and Non Linear auto-regressive with exogenous variable (NARX) data driven models to forecast groundwater level for multiple input scenario's and also multiple lead time. Weekly time series groundwater level data has been used as input and the models are developed to forecast one, two, three, four, five and sixth week ahead. Root mean square error (RMSE) and correlation coefficient (Cc) are used for evaluating the accuracy of the models. Based on the comparison of results, it was found that the RBF models are superior to the NARX models in forecasting groundwater level considering RMSE and Cc. The obtained result indicates that the RBF has high performance and consistent upto fourth week lead time and decaying performance for NARX models. Hence, RBF and NARX have the potential in forecasting groundwater level efficiently for multi step lead time. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting(Elsevier B.V., 2017) Rezaie-Balf, M.; Naganna, S.R.; Ghaemi, A.; Deka, P.C.In this study, two different machine learning models, Multivariate Adaptive Regression Splines (MARS) and M5 Model Trees (MT) have been applied to simulate the groundwater level (GWL) fluctuations of three shallow open wells within diverse unconfined aquifers. The Wavelet coupled MARS and MT hybrid models were developed in an attempt to further increase the GWL forecast accuracy. The Discrete Wavelet Transform (DWT) which is particularly effective in dealing with non-stationary time-series data was employed to decompose the input time series into various sub-series components. Historical data of 10 years (August-1996 to July-2006) comprising monthly groundwater level, rainfall, and temperature were used to calibrate and validate the models. The models were calibrated and tested for one, three and six months ahead forecast horizons. The wavelet coupled MARS and MT models were compared with their simple counterpart using standard statistical performance evaluation measures such as Root Mean Square Error (RMSE), Normalized Nash-Sutcliffe Efficiency (NNSE) and Coefficient of Determination (R2). The wavelet coupled MARS and MT models developed using multi-scale input data performed better compared to their simple counterpart and the forecast accuracy of W-MARS models were superior to that of W-MT models. Specifically, the DWT offered a better discrimination of non-linear and non-stationary trends that were present at various scales in the time series of the input variables thus crafting the W-MARS models to provide more accurate GWL forecasts. © 2017 Elsevier B.V.