Browsing by Author "Naganna, N.S."

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Experimental investigation on utilization of waste shredded rubber tire as a replacement to fine aggregate in concrete
(Springer, 2019) Hiremath, P.N.; Jayakesh, K.; Rai, R.; Naganna, N.S.; Yaragal, S.C.
Depletion of natural resources in the past few decades due to rapid construction activities all around the world has forced a threat to the availability of natural resources for future generation. The utilization of waste industrial by products, in the form of supplementary cementitious materials and waste tire rubber products replacing natural aggregates in production of concrete. In the present study performance of concrete mixes incorporating 2.5, 5, 7.5 and 10% Waste Shredded Rubber Tire (WSRT) as partial replacement of fine aggregate is investigated. Numerous research works have been conducted on replacement of aggregate by waste crumb rubber but data scarce on utilization of waste rubber in concrete directly. Hence to examine characteristics of shredded rubber tire based concretes, two sets of concrete specimen were produced. In the first set, shredded rubber tire is added directly without any pretreatment and in the second set the shredded rubber tire was immersed in NaOH solution for 24 h and then washed with water thoroughly and rubbed with sand paper to obtain the rough surface finish to facilitate improved bonding properties with cement matrix. To evaluate the performance of WSRT based concretes, fresh and hardened properties were determined by conducting slump tests on fresh mixes, and compression, flexural and impact tests on hardened concrete cubes and prisms. Proving results were obtained for potential use of WSRT in concretes for generalized applications. © Springer Nature Singapore Pte Ltd. 2019.
Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression
(Cogent OA info@CogentOA.com, 2015) Naganna, N.S.; Deka, P.C.
This research demonstrates the state-of-the-art capability of Wavelet packet analysis in improving the forecasting efficiency of Support vector regression (SVR) through the development of a novel hybrid Wavelet packet–Support vector regression (WP–SVR) model for forecasting monthly groundwater level fluctuations observed in three shallow unconfined coastal aquifers. The Sequential Minimal Optimization Algorithm-based SVR model is also employed for comparative study with WP–SVR model. The input variables used for modeling were monthly time series of total rainfall, average temperature, mean tide level, and past groundwater level observations recorded during the period 1996–2006 at three observation wells located near Mangalore, India. The Radial Basis function is employed as a kernel function during SVR modeling. Model parameters are calibrated using the first seven years of data, and the remaining three years data are used for model validation using various input combinations. The performance of both the SVR and WP–SVR models is assessed using different statistical indices. From the comparative result analysis of the developed models, it can be seen that WP–SVR model outperforms the classic SVR model in predicting groundwater levels at all the three well locations (e.g. NRMSE(WP–SVR) = 7.14, NRMSE(SVR) = 12.27; NSE(WP–SVR) = 0.91, NSE(SVR) = 0.8 during the test phase with respect to well location at Surathkal). Therefore, using the WP–SVR model is highly acceptable for modeling and forecasting of groundwater level fluctuations. © 2015 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Multistep ahead groundwater level time-series forecasting using gaussian process regression and ANFIS
(Springer Verlag service@springer.de, 2016) Naganna, N.S.; Deka, P.C.
Groundwater level is regarded as an environmental indicator to quantify groundwater resources and their exploitation. In general, groundwater systems are characterized by complex and nonlinear features. Gaussian Process Regression (GPR) approach is employed in the present study to investigate its applicability in probabilistic forecasting of monthly groundwater level fluctuations at two shallow unconfined aquifers located in the Kumaradhara river basin near Sullia Taluk, India. A series of monthly groundwater level observations monitored during the period 2000â€“2013 is utilized for the simulation. Univariate time-series GPR and Adaptive Neuro Fuzzy Inference System (ANFIS) models are simulated and applied for multistep lead time forecasting of groundwater levels. Individual performance of the GPR and ANFIS models are comparatively evaluated using various statistical indices. In overall, simulation results reveal that GPR model provided reasonably accurate predictions than that of ANFIS during both training and testing phases. Thus, an effective GPR model is found to generate more precise probabilistic forecasts of groundwater levels. Â© Springer India 2016.