Browsing by Author "Deka, P.C."

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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).
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.
An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs
(Elsevier B.V., 2016) Patil, A.P.; Deka, P.C.
Precise estimation of evapotranspiration is crucial for accurate crop-water estimation. Recently machine learning (ML) techniques like artificial neural network (ANN) are being widely used for modeling the process of evapotranspiration. However, ANN faces issues like trapping in local minima, slow learning and tuning of meta-parameters. In this study an improved extreme learning machine (ELM) algorithm was used to estimate weekly reference crop evapotranspiration (ETo). The study was carried out for Jodhpur and Pali meteorological weather stations located in the Thar Desert, India. The study evaluated the performance of three different input combinations. The first input combination used locally available maximum and minimum air temperature data while the second and third combination used ETo values from another station (extrinsic inputs) along with the locally available temperature data as inputs. The performance of ELM models was compared with the empirical Hargreaves equation, ANN and least-square support vector machine (LS-SVM) models. Root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE) and threshold statistics (TS) were used for comparing the performance of the models. The performance of ELM model was found to be better than the Hargreaves and ANN model. The LS-SVM and ELM displayed similar performance. ELM3 models, with 36 and 33 neurons in hidden layer were found to be the best models (RMSE of 0.43 for Jodhpur and 0.33 for Pali station) for estimating weekly ETo at Jodhpur and Pali stations respectively. The results showed that ELM is a simple yet efficient algorithm which exhibited good performance; hence, can be recommended for estimating weekly ETo. Furthermore, it was also found that use of ETo values from another station can help in improving the efficiency of ML models in limited data scenario. © 2016 Elsevier B.V.
Application of Mamdani model-based fuzzy inference system in water consumption estimation using time series
(Springer Science and Business Media Deutschland GmbH, 2022) Surendra, H.J.; Deka, P.C.; Rajakumara, H.N.
Artificial intelligence methods resemble human thinking structure that are used in hydrological modeling. In this work, water consumption estimation modeling is done using Mamdani fuzzy inference system. Different combinations of the models were developed by changing structures scenario such as: membership function, rules criteria, fuzzy set and defuzzification method. Mapping of input and output function are done using climatic variables and water consumption data. Rainfall, maximum temperature, minimum temperature and relative humidity were used as input factors and water consumption as output function. The reasoning mechanism of the fuzzy inference system calculates the recommended value of water consumption. Obtained value is compared with the actual recommended values to determine the usefulness of the system. The performances of the models were evaluated using performance indices such as correlation coefficient, mean square error and mean relative error. Results highlight that Mamdani fuzzy inference system is effective in actual application. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Artificial intelligence application in drought assessment, monitoring and forecasting: a review
(Springer Science and Business Media Deutschland GmbH, 2022) Kikon, A.; Deka, P.C.
Drought is a natural hazard creating havoc on economic, social and environmental aspects. As a result of its slow and creeping nature, it is problematic to establish the onset as well as the termination of drought. Irrespective of its spatial and temporal variability, drought occurs in almost all regions. A wide range of drought studies has been conducted by many researchers over a long period of time. The damage caused by drought has a huge impact on the social, economic and agricultural sectors. Researchers have defined drought in different ways depending upon the parameters and its characteristics, and universally there is no proper definition for drought because of its complexity in nature. This review is focused mainly on various Artificial Intelligence techniques used in drought assessment, monitoring, management and forecasting. The findings from the study shows that drought prediction has become significance in the field of hydrology, Water Resources Management, sustainable agriculture, etc. by using the various AI techniques. In recent studies, AI has been used widely in analysing drought in different regions. The applications of AI techniques in the domain of drought assessing, monitoring, forecasting, etc., shows a rapid growth and that the impact of these will be increasing in future. For understanding the different concepts of drought study, it is needed to establish different system of drought management in order to monitor the different factors affecting drought and then take proper measures to mitigate the damage. Literature studies have been done to analyze the onset and other measures of drought management. Future research may be oriented towards Modeling and probabilistic analysis of climatic data for refining the drought vulnerability mapping, analysis of onset and termination, warning system and drought declaration process depending on the conditions of the region. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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.
Assessing the impact of 2018 tropical rainfall and the consecutive flood-related damages for the state of Kerala, India
(Elsevier, 2021) Kulithalai Shiyam Sundar, K.S.S.; Deka, P.C.; Subbarayan, S.; Devanantham, D.; Jacinth Jennifer, J.
Flood is the relatively high flow in the river, markedly than the usual resulting in the inundation of low land. Usually, river floods when the river can no longer contain its discharge from its catchments. Flood is the costliest as well as a common natural disaster in the world devastating both life and economy to a greater extent. The state of Kerala has faced an unprecedented rainfall followed by severe floods in August 2018 with a death toll for 504. Kerala is the southernmost narrow strip of the coastal territory that slopes down the Western Ghats to reach the Arabian Sea with 14 districts in the state. According to the Central Water Commission (CWC), the state received 2346.6 mm of rain from June to 19th of August, which is 42% more than the average rainfall. The state received a tremendous rainfall of 758.6 mm in the first 20 days of August which is 164% more than the actual rainfall. With the heavy rainfall all over the state, floods prevailed by the end of July. Once again a massive spell of rainfall happened at 8th and 9th of August which led to further flooding in Wayanad district. Due to the continuous rainfall from the first week of June to August, water levels were almost near the Full Reservoir Level. So, the water was released from several dams due to the heavy rainfall in the catchment. Another intense spell of rainfall took place by the 14th of August and continued till 19th of August resulting in the massive flood throughout the state affecting 13 of the 14 districts leading to the evacuation of about 3.4 million people to the 12, 300 relief camp across the state making the worst flood in the century. 2018 Kerala flood caused extensive damage to the crops, building, and infrastructure; its associated aftermath of the flood resulted in a huge loss to its economic, social, and natural environment, accompanied by the 331 landslides across 10 districts. After ravaging by the flood, the state has faced communicable diseases leptospirosis, chicken pox, hepatitis A, malaria, and dengue resulting in a death toll for 180. Thus, this paper is tried to understand the impact of the tropical rainfall followed by the devastating flood that occurred in the state of Kerala in August 2018 and to understand the impact on the socioeconomic disturbances, its resilience aftermath the flood. © 2021 Elsevier Inc. All rights reserved.
Assessment of potentially vulnerable zones using geospatial approach along the coast of Cuddalore district, East coast of India
(Taylor and Francis Ltd., 2022) Kulithalai Shiyam Sundar, K.S.S.; Subbarayan, S.; Deka, P.C.; Devanantham, A.
Coastal zones constantly undergo rapid changes in shape, morphology, and the environment due to natural as well as human development activities. Thus, assessing the vulnerability of the coast has become an important matter of concern. The study area is about 33 km of coastal zone from the Gaddilam to the Vellar River of Cuddalore districts in Tamil Nadu, India. This region was affected during the 2004 tsunami that took place in the Indian Ocean and also influenced by many cyclones in the Bay of Bengal. The methodology is about preparing various thematic layers such as shoreline change, elevation data, coastal slope, bathymetry, mean tidal range, maximum surge, beach width, geomorphology, and sea-level rise. Rank and weights are assigned to these parameters using the Index Overlay method in Geographic Information System environment. Vulnerability zones of different magnitudes such as very high, high, moderate, low, and very low were classified. From the study it is found about 15% of the coast is under very high vulnerability, 10.2% of the study lies under high vulnerability, 35.4% of the study lies under the moderately vulnerable region, 24% and 15.4% of the area lies under low and very low vulnerable region, respectively. © 2020 Indian Society for Hydraulics.
Classification of case-II waters using hyperspectral (HICO) data over North Indian Ocean
(2016) Srinivasa, Rao, N.; Ramarao, E.P.; Srinivas, K.; Deka, P.C.
State of the art Ocean color algorithms are proven for retrieving the ocean constituents (chlorophyll-a, CDOM and Suspended Sediments) in case-I waters. However, these algorithms could not perform well at case-II waters because of the optical complexity. Hyperspectral data is found to be promising to classify the case-II waters. The aim of this study is to propose the spectral bands for future Ocean color sensors to classify the case-II waters. Study has been performed with Rrs's of HICO at estuaries of the river Indus and GBM of North Indian Ocean. Appropriate field samples are not available to validate and propose empirical models to retrieve concentrations. The sensor HICO is not currently operational to plan validation exercise. Aqua MODIS data at case-I and Case-II waters are used as complementary to in- situ. Analysis of Spectral reflectance curves suggests the band ratios of Rrs 484 nm and Rrs 581 nm, Rrs 490 nm and Rrs 426 nm to classify the Chlorophyll -a and CDOM respectively. Rrs 610 nm gives the best scope for suspended sediment retrieval. The work suggests the need for ocean color sensors with central wavelength's of 426, 484, 490, 581 and 610 nm to estimate the concentrations of Chl-a, Suspended Sediments and CDOM in case-II waters. � 2016 SPIE.
Classification of case-II waters using hyperspectral (HICO) data over North Indian Ocean
(SPIE spie@spie.org, 2016) Srinivasa Rao, N.; Ramarao, E.P.; Srinivas, K.; Deka, P.C.
State of the art Ocean color algorithms are proven for retrieving the ocean constituents (chlorophyll-a, CDOM and Suspended Sediments) in case-I waters. However, these algorithms could not perform well at case-II waters because of the optical complexity. Hyperspectral data is found to be promising to classify the case-II waters. The aim of this study is to propose the spectral bands for future Ocean color sensors to classify the case-II waters. Study has been performed with Rrs's of HICO at estuaries of the river Indus and GBM of North Indian Ocean. Appropriate field samples are not available to validate and propose empirical models to retrieve concentrations. The sensor HICO is not currently operational to plan validation exercise. Aqua MODIS data at case-I and Case-II waters are used as complementary to in- situ. Analysis of Spectral reflectance curves suggests the band ratios of Rrs 484 nm and Rrs 581 nm, Rrs 490 nm and Rrs 426 nm to classify the Chlorophyll -a and CDOM respectively. Rrs 610 nm gives the best scope for suspended sediment retrieval. The work suggests the need for ocean color sensors with central wavelength's of 426, 484, 490, 581 and 610 nm to estimate the concentrations of Chl-a, Suspended Sediments and CDOM in case-II waters. Â© 2016 SPIE.
Comparison of Oceansat-2 scatterometer- to buoy-recorded winds and spatial distribution over the Arabian Sea during the monsoon period
(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2015) Gadad, S.; Deka, P.C.
For this wind resource assessment (WRA) study, wind speed and direction are the fundamental inputs. Also, these studies are data driven and require large historical wind speed data sets available on the site. This work explores the application of space-based scatterometer winds for assimilation into WRA studies towards the development of offshore wind energy. This article focuses on estimating the performance of Oceansat-2 scatterometer (OSCAT)-derived wind vector using in situ data from buoys at different locations in the Arabian Sea. A comparative study between three methods for estimating the equivalent neutral winds (ENW) for buoys is carried out. OSCAT winds were closest to ENW estimated by the Liu–Katsaros–Businger (LKB) method. The spatial and temporal windows for comparison were 0.5° and ±60 minutes, respectively. The monsoon months (June–September) of 2011 were selected for study. The root mean square deviation for wind speed is less than 2.5 m s?1 and wind direction is less than 20°, and a small positive bias is observed in the OSCAT wind values. From the analysis, the OSCAT wind values are consistent with in situ-observed values. Furthermore, wind atlas maps were developed with OSCAT winds, representing the spatial distribution of winds at a height of 10 m over the Arabian Sea. © 2015 Taylor & Francis.
Daily pan evaporation modeling in climatically contrasting zones with hybridization of wavelet transform and support vector machines
(Springer Verlag service@springer.de, 2017) Pammar, L.; Deka, P.C.
The estimation of evaporation has been under surveillance, which is being carried out by many researchers toward applications in the fields related to hydrology and water resources management. Due to complexities associated with its estimation, research has employed several modes via direct and indirect methods to estimate. Accurate estimations are still the thrust area of research in these fields. The pan evaporation estimations with the help of data modeling techniques have provided better results in the recent past. The advancement in the field of data modeling has introduced several techniques which can best fit the data type and provide accurate estimations. The novel gamma test (GT) was used to decide the best input–output combination. Parameter optimization was carried out by grid search. The developed models gave better estimations of pan evaporation, but exhibited some limitations with nonlinearity, and sparse and noisy data. These limitations paved way for data pre-processing techniques such as wavelet transform. This study made an attempt to explore hybrid modeling using discrete wavelet transform (DWT) and support vector machines (SVR) for pan evaporation estimation. Two stations representing contrasting climatic zones namely ‘Bajpe’ and ‘Bangalore’ located in the state of Karnataka, India, are selected in this study. The meteorological datasets recorded at these stations are analyzed using gamma test and grid search to use the best input–output combinations for the models. The modeled pan evaporation estimations are very promising toward ever demanding accuracy expected in the associated fields. © 2017, The International Society of Paddy and Water Environment Engineering and Springer Japan.
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.
Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time
(2012) Deka, P.C.; Prahlada, R.
Recently Artificial Neural network (ANN) was extensively used as non-linear inter-extrapolator for ocean wave forecasting as well as other application in ocean engineering. In this current study, the Wavelet transform was hybridised with ANN naming Wavelet Neural Network (WLNN) for significant wave height forecasting near Mangalore, west coast of India, upto 48 h lead time. The main time series of significant wave height data were decomposed to multiresolution time series using discrete wavelet transformations. Then, the multiresolution time series data were used as input of the ANN to forecast the significant wave height at different multistep lead time. It was shown how the proposed model, WLNN, that makes use of multiresolution time series as input, allows for more accurate and consistent predictions with respect to classical ANN models. The proposed wavelet model (WLNN) results revealed that it was better forecasted and consistent than single ANN model because of using multiresolution time series data as inputs. © 2012 Elsevier Ltd. All rights reserved.
Discrete wavelet-Ann approach in time series flow forecasting-a case study of Brahmaputra river
(2012) Deka, P.C.; Haque, L.; Banhatti, A.G.
This paper deals with the prediction of hydrologic behavior of the runoff for the one of the largest discharge carrier International River, Brahmaputra, located in Assam (India) at the Pandu station, by using daily time unit. The flow regime dominated by high data non-stationary and seasonal irregularity due to Himalayan climate fallout. The influence of data preprocessing through wavelet transforms has been investigated. For this, the main time series of flow data were decomposed to multi resolution time series using discrete wavelet transformations. Then these decomposed data were used as input to Artificial Neural Network (ANN) for multiple lead time flow forecasting. Various types of wavelets were used to evaluate the optimal performance of models developed. The forecasting accuracy of the models has been tested for multiple lead time upto 4 days using different decomposition levels. The performance of the proposed hybrid model has been evaluated based on the performance indices such as root mean square error (RMSE), coefficient of efficiency (CE) and mean relative error (MRE).The results shows the better forecasting accuracy by the proposed combined hybrid model over the single ANN model in hydrological time series forecasting. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.
Effect of roughness coefficient on discharge and flow depth by using hydraulic model for nethravathi river Basin, India
(Inderscience Publishers, 2021) Kappadi, P.; Nagaraj, M.K.; Deka, P.C.
The river stage and discharge are dynamic due to various factors affecting the flow characteristics in a natural channel. The channel roughness plays an important role since it is not a constant parameter and varies along the length of the river. The objective of the present study is to assess the variation of Manning's roughness coefficient on flow characteristics of Nethravathi River. In the study, 1D Saint-Venant equation-based HEC-RAS hydraulic model was used to simulate the effect of roughness coefficient (Manning's coefficient n) on discharge and stage of river flow. The model result showed good consensus between model computed flow discharge values and observed flow discharge measured at downstream gauging station. The study found that the computed stage values increased whereas the associated peak discharge decreased with the increase in Manning's roughness coefficient. The stage-discharge rating curves revealed that Manning's n value is relatively more sensitive at higher discharge values. © 2021 Inderscience Enterprises Ltd.
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.
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.
Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting
(Springer Netherlands rbk@louisiana.edu, 2019) Sulugodu, B.; Deka, P.C.
Streamflow forecasting can offer valuable information for optimal management of water resources, flood mitigation, and drought warning. This research aims in evaluating the effectiveness of CHIRPS satellite rainfall data in comparison with IMD gridded Rainfall Data and development of various flow forecasting models. Daily rainfall data for three decades (1983–2012) over the Nethravathi Basin, Karnataka, India is used for analysis. The analysis is carried out for the monsoon season (June–September), out of which 70% data considered for training the model and remaining for testing. Different input combinations are developed, and soft-computing methods like ANFIS, GRNN, PSO-ANN, and ELM are applied for flow forecasting on a temporal scale. The model performance is evaluated using various statistical indices like NNSE, RRMSE, and MAE. The results indicate that CHIRPS rainfall showed better performance in comparison with IMD data. ELM expressed an enhanced effect when compared to all other methods. The usefulness and effectiveness of CHIRPS data compared to IMD data has been explored. © 2019, Springer Nature B.V.
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.