Faculty Publications
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Item A basic review of fuzzy logic applications in hydrology and water resources(Springer Science and Business Media Deutschland GmbH, 2020) Kambalimath S, S.; Deka, P.C.In recent years, fuzzy logic has emerged as a powerful technique in the analysis of hydrologic components and decision making in water resources. Problems related to hydrology often deal with imprecision and vagueness, which can be very well handled by fuzzy logic-based models. This paper reviews a variety of applications of fuzzy logic in the domain of hydrology and water resources in brief. So far in the literature, fuzzy logic-based hybrid models have been significantly applied in hydrologic studies. Furthermore, in this paper, the literature is reviewed on the basis of applications using pure fuzzy logic models and applications using hybrid-fuzzy modeling approach. This review suggests that hybrid-fuzzy modeling approach works well in many applications of hydrology when compared with pure fuzzy logic modeling. © 2020, The Author(s).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 Estimation of saturated hydraulic conductivity using fuzzy neural network in a semi-arid basin scale for murum soils of India(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) More, S.B.; Deka, P.C.Saturated hydraulic conductivity, Ks is an important input parameter in modeling flow process in soil. Measurement of Ks in field is time consuming and costly. Also, due to inherent temporal and spatial variability of this parameter, large number of samples are required to characterize the areas of site. In this study, a hybrid approach consists of Fuzzy Neural Network (FNN), has been proposed to estimate Ks from limited number of field measurements using Guelph permeameter. The various soil properties such as bulk density, porosity, specific gravity, sand, clay, silt and organic matter were used as input variables and Ks was kept as output. In this study, 175 field measurements and soil samples were collected in a grid of 40 m × 200 m with uniform spacing along the slope of barren land in the site of Punanaka (Solapur city), India. To quantify the prediction accuracy, this FNN approach is compared with regression, Fuzzy Mamdani approach and artificial neural network with BP algorithm. The various statistical performance indices like root mean square error, coefficient of determination (R2), and Mean relative error were used for evaluation of model performance. It was found that the hybrid FNN approach in comparison with others could more accurately predict saturated hydraulic conductivity. © 2017 Indian Society for Hydraulics.Item Fuzzy and improved fuzzy-wavelet approach in modeling municipal residential water consumption estimation using climatic variables(Springer, 2020) Surendra, H.J.; Deka, P.C.This work highlights the importance of fuzzy-wavelet denoise and fuzzy-wavelet compress in modeling the municipal residential water consumption estimation. To begin, fuzzy logic is used with different rules, membership criteria and fuzzy set. Based on accuracy of the developed model, optimum number of rules and best membership function were selected. To improve the accuracy of the single fuzzy model, wavelets technique (denoise and compress approach) was coupled with fuzzy logic and results were compared to single fuzzy technique. To map the input and output functions, the present research work includes Mamdani fuzzy inference approach based on various climatic input variables like rainfall, maximum temperature, minimum temperature and relative humidity. The models were trained based on climatic data to a certain period, and corresponding estimated models were tested for the same period. Result highlights that models with denoise and compress approach have better accuracy compared to single fuzzy model. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.