Faculty Publications
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Item Factors influencing streambed hydraulic conductivity and their implications on stream–aquifer interaction: a conceptual review(Springer Verlag service@springer.de, 2017) Naganna, S.R.; Deka, P.C.; Ch, S.; Hansen, W.F.The estimation and modeling of streambed hydraulic conductivity (K) is an emerging interest due to its connection to water quality, aquatic habitat, and groundwater recharge. Existing research has found ways to sample and measure K at specific sites and with laboratory tests. The challenge undertaken was to review progress, relevance, complexity in understanding and modeling via statistical and geostatistical approaches, literature gaps, and suggestions toward future needs. This article provides an overview of factors and processes influencing streambed hydraulic conductivity (K) and its role in the stream–aquifer interaction. During our synthesis, we discuss the influence of geological, hydrological, biological, and anthropogenic factors that lead to variability of streambed substrates. Literature examples document findings to specific sites that help to portray the role of streambed K and other interrelated factors in the modeling of hyporheic and groundwater flow systems. However, studies utilizing an integrated, comprehensive database are limited, restricting the ability of broader application and understanding. Examples of in situ and laboratory methods of estimating hydraulic conductivity suggest challenges in acquiring representative samples and comparing results, considering the anisotropy and heterogeneity of fluvial bed materials and geohydrological conditions. Arriving at realistic statistical and spatial inference based on field and lab data collected is challenging, considering the possible sediment sources, processes, and complexity. Recognizing that the K for a given particle size group includes several to many orders of magnitude, modeling of streambed K and groundwater interaction remain conceptual and experimental. Advanced geostatistical techniques offer a wide range of univariate or multi-variate interpolation procedures such as kriging and variogram analysis that can be applied to these complex systems. Research available from various studies has been instrumental in developing sampling options, recognizing the significance of fluvial dynamics, the potential for filtration, transfer, and storage of high-quality groundwater, and importance to aquatic habitat and refuge during extreme conditions. Efforts in the characterization of natural and anthropogenic conditions, substrate materials, sediment loading, colmation, and other details highlight the great complexity and perhaps need for a database to compile relevant data. The effects on streambed hydraulic conductivity due to anthropogenic disturbances (in-stream gravel mining, contaminant release, benthic activity, etc.) are the areas that still need focus. An interdisciplinary (hydro-geo-biological) approach may be necessary to characterize the magnitude and variability of streambed K and fluxes at local, regional scales. © 2017, Springer-Verlag GmbH Germany.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.Item Eco-concrete for sustainability: utilizing aluminium dross and iron slag as partial replacement materials(Springer Verlag, 2017) Javali, S.; Chandrashekar, A.R.; Naganna, S.R.; Manu, D.S.; Hiremath, P.N.; Preethi, H.G.; Vinod Kumar, N.Emphasis on utilizing the industrial waste/discarded materials can be brought about by discovering innovative methods of disposal. One such a way of waste disposal can be through utilizing them in concrete production as a filler material or pozzolana. In this regard, the present study proposes to use the aluminium dross and granular iron slag as partial replacement materials for cement and natural sand, respectively, to develop eco-concrete. Nine mixes were produced with different proportions of cement, aluminium dross, sand and granular iron slag content. The aluminium dross was replaced at 5, 10, 15 and 20% of the weight of the cement. Initially, the optimal substitution percentage of aluminium dross was found without the substitution of iron slag based on the strength results. Later, by adopting the optimal aluminium dross percentage with cement, the granular iron slag was partially substituted at 10, 20, 30 and 40% of natural sand to find the overall optimal blend. The strength and durability properties of the M40 grade concrete employing these two admixture combinations were analysed. It was noticed that the strength and durability properties of the eco-concrete produced by incorporating aluminium dross ?5% and iron slag ?20% were comparable to that of conventional concrete. Furthermore, from the toxicity analysis, it was seen that the leaching of heavy and trace elements from the eco-concrete was negligibly small and within the limits. In near future, the cost-effective, eco-friendly materials and technologies can be opted as a perpetual strategy to overcome severe material shortages for resource conservation and economy. © 2017, Springer-Verlag GmbH Germany.Item Estimation of dew point temperature using SVM and ELM for humid and semi-arid regions of India(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Deka, P.C.; Patil, A.P.; Yeswanth Kumar, P.; Naganna, S.R.The dew point temperature is the temperature at which the moisture in the air begins to condense into dew or water droplets. The accurate estimation of the dew point temperature is very important as it controls the heat stress on humans, detects fluctuations of evaporation rates, and humidity trends. The dew point temperature is a significant parameter particularly required in various hydrological, climatological and agronomical related researches. This study proposes Support Vector Machine (SVM) and Extreme Learning Machine (ELM) models for the estimation of daily dew point temperature. The daily measured weather data (Wet bulb temperature, relative humidity, vapor pressure and dew point temperature) of humid and semi-arid regions of India were used for model development. The statistical indices, namely Mean Absolute Error, Root Mean Square Error, and Nash Sutcliffe Efficiency were adopted to evaluate the performances of these two models. The merit of the ELM model is evaluated against SVM technique in the estimation of dew point temperature. The proposed ELM models demonstrated much greater capability than the SVM models in the estimation of daily dew point temperature. © 2017 Indian Society for Hydraulics.Item Variability of streambed hydraulic conductivity in an intermittent stream reach regulated by Vented Dams: A case study(Elsevier B.V., 2018) Naganna, S.R.; Deka, P.C.The hydro-geological properties of streambed together with the hydraulic gradients determine the fluxes of water, energy and solutes between the stream and underlying aquifer system. Dam induced sedimentation affects hyporheic processes and alters substrate pore space geometries in the course of progressive stabilization of the sediment layers. Uncertainty in stream-aquifer interactions arises from the inherent complex-nested flow paths and spatio-temporal variability of streambed hydraulic properties. A detailed field investigation of streambed hydraulic conductivity (Ks) using Guelph Permeameter was carried out in an intermittent stream reach of the Pavanje river basin located in the mountainous, forested tract of western ghats of India. The present study reports the spatial and temporal variability of streambed hydraulic conductivity along the stream reach obstructed by two Vented Dams in sequence. Statistical tests such as Levene's and Welch's t-tests were employed to check for various variability measures. The strength of spatial dependence and the presence of spatial autocorrelation among the streambed Ks samples were tested by using Moran's I statistic. The measures of central tendency and dispersion pointed out reasonable spatial variability in Ks distribution throughout the study reach during two consecutive years 2016 and 2017. The streambed was heterogeneous with regard to hydraulic conductivity distribution with high-Ks zones near the backwater areas of the vented dam and low-Ks zones particularly at the tail water section of vented dams. Dam operational strategies were responsible for seasonal fluctuations in sedimentation and modifications to streambed substrate characteristics (such as porosity, grain size, packing etc.), resulting in heterogeneous streambed Ks profiles. The channel downstream of vented dams contained significantly more cohesive deposits of fine sediment due to the overflow of surplus suspended sediment-laden water at low velocity and pressure head. The statistical test results accept the hypothesis of significant spatial variability of streambed Ks but refuse to accept the temporal variations. The deterministic and geo-statistical approaches of spatial interpolation provided virtuous surface maps of streambed Ks distribution. © 2018 Elsevier B.V.Item Dew Point temperature estimation: Application of artificial intelligence model integrated with nature-inspired optimization algorithms(MDPI AG indexing@mdpi.com Postfach Basel CH-4005, 2019) Naganna, S.R.; Deka, P.C.; Ghorbani, M.A.; Biazar, S.M.; Al-Ansari, N.; Yaseen, Z.M.Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro-climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP-FFA and MLP-GSA) were authenticated against standard MLP tuned by a Levenberg-Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones. © 2019 by the authors.Item Artificial intelligence approaches for spatial modeling of streambed hydraulic conductivity(Springer International Publishing, 2019) Naganna, S.R.; Deka, P.C.Saturated hydraulic conductivity (Ks) describes the water movement through saturated porous media. The hydraulic conductivity of streambed varies spatially owing to the variations in sediment distribution profiles all along the course of the stream. The artificial intelligence (AI) based spatial modeling schemes were instituted and tested to predict the spatial patterns of streambed hydraulic conductivity. The geographical coordinates (i.e., latitude and longitude) of the sampled locations from where the in situ hydraulic conductivity measurements were determined were used as model inputs to predict streambed Ks over spatial scale using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) paradigms. The statistical measures computed by using the actual versus predicted streambed Ks values of individual models were comparatively evaluated. The AI-based spatial models provided superior spatial Ks prediction efficiencies with respect to both the strategies/schemes considered. The model efficiencies of spatial modeling scheme 1 (i.e., Strategy 1) were better compared to Strategy 2 due to the incorporation of more number of sampling points for model training. For instance, the SVM model with NSE = 0.941 (Strategy 1) and NSE = 0.895 (Strategy 2) were the best among all the models for 2016 data. Based on the scatter plots and Taylor diagrams plotted, the SVM model predictions were found to be much efficient even though, the ANFIS predictions were less biased. Although ANN and ANFIS models provided a satisfactory level of predictions, the SVM model provided virtuous streambed Ks patterns owing to its inherent capability to adapt to input data that are non-monotone and nonlinearly separable. The tuning of SVM parameters via 3D grid search was responsible for higher efficiencies of SVM models. © 2019, Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.Item Hybrid wavelet packet machine learning approaches for drought modeling(Springer, 2020) Das, P.; Naganna, S.R.; Deka, P.C.; Pushparaj, J.Among all the natural disasters, drought has the most catastrophic encroachment on the surrounding and environment. Gulbarga, one of the semi-arid districts of Karnataka state, India receives about 700 mm of average annual rainfall and is drought inclined. In this study, the forecasting of drought for the district has been carried out for a lead time of 1 month and 6 months. The multi-temporal Standardized Precipitation Index (SPI) has been used as the drought quantifying parameter due to the fact that it is calculated on the basis of one simplest parameter, i.e., rainfall and additionally due to its ease of use. The fine resolution daily gridded precipitation data (0.25º × 0.25º) procured from Indian Meteorological Department (IMD) of 21 grid locations within the study area have been used for the analysis. Forecasting of drought plays a significant role in drought preparedness and mitigation plans. With the advent of machine learning (ML) techniques over the past few decades, forecasting of any hydrologic event has become easier and more accurate. However, the use of these techniques for drought forecasting is still obscure. In this study, Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques have been employed to examine their accuracy in drought forecasting over shorter and longer lead times. Furthermore, two hybrid approaches have been formulated by coupling a data transformation method with each of the aforementioned ML approaches. At the outset, pre-processing of input data (i.e., SPI) has been carried out using Wavelet Packet Transform (WPT) and then used as inputs to ANN and SVR models to induce hybrid WP-ANN and WP-SVR models. The performance of the hybrid models has been evaluated based on the statistical indices such as R2 (co-efficient of determination), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error). The results showed that the hybrid techniques have better forecast performance than the standalone machine learning approaches. Hybrid WP-ANN model performed relatively better than WP-SVR model for most of the grid locations. Also, the forecasting results deteriorated as the lead time increased from 1 to 6 months. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.Item Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India(Springer, 2022) More, S.B.; Deka, P.C.; Patil, A.P.; Naganna, S.R.Saturated hydraulic conductivity (Kfs) is the major parameter that affects the movement of water and solutes in soil strata. Although one can estimate the Kfs directly by using various field or laboratory methods, they turn out to be more time-consuming and painstaking while characterizing the spatial variability of Kfs. For this reason, some recent researches employ indirect approaches such as pedotransfer functions (PTF) and surface modeling methods for estimating Kfs of several scales. Pedotransfer functions are often developed by relating the Kfs with readily available soil properties such as bulk density, porosity, sand content, silt content, and organic material. The present research explores the suitability of Extreme Learning Machine (ELM) in developing PTF's for Kfs by using basic soil properties. In-situ field tests and laboratory experiments on collected samples were performed to acquire the datasets necessary for the analysis. Three competitive soft computing approaches, namely the ELM, Support Vector Machine (SVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) based on Fuzzy C-means Clustering optimized by Genetic Algorithm were exercised for developing the Kfs models. Further, the performance of these approaches in modeling Kfs was evaluated using various statistical mertics. The performance of ELM was found to be good in comparison to the other two models, with sufficiently good NSE values. The ELM model provided Kfs predictions at the Murarji Peth and Punanaka sites with an NSE of 0.90 and 0.83, respectively, while at the Mulegoan site, the ANFIS model was better with R = 0.80 and NSE = 0.64. © 2022, Indian Academy of Sciences.Item ANFIS-based soft computing models for forecasting effective drought index over an arid region of India(IWA Publishing, 2023) Kikon, A.; Dodamani, B.M.; Deb Barma, S.D.; Naganna, S.R.Drought is a natural hazard that is characterized by a low amount of precipitation in a region. In order to evaluate the drought-related issues that cause chaos for human well-being, drought indices have become increasingly important. In this study, the monthly precipitation data from 1964 to 2013 (about 50 years) of the Jodhpur district in the drought-prone Rajasthan state of India was used to derive the effective drought index (EDI). The machine learning models hybridized with evolutionary optimizers such as the genetic algorithm adaptive neuro-fuzzy inference system (GA-ANFIS) and particle swarm optimization ANFIS (PSO-ANFIS) were used in addition to the generalized regression neural network (GRNN) to predict the EDI index. Using the partial autocorrelation function (PACF), models for forecasting the monthly EDI were constructed with 2-, 3- and 5-input combinations to evaluate their outcomes based on various performance indices. The results of the different combination models were compared. With reference to 2-input and 3-input combination models, both GA-ANFIS and PSO-ANFIS show better performance results with R2 ¼ 0.75, while among the models with 5-input combination, GA-ANFIS depicts better performance results compared to other models with R2 ¼ 0.78. The results are presented suitably with the aid of scatter plots, Taylor’s diagram and violin plots. Overall, the GA-ANFIS and PSO-ANFIS models outperformed the GRNN model. © 2023 The Authors.