Informatics Publishing Limited

S. M. Ramasamy ¹, S. Anbazhagan ¹

Affiliations
1 Centre for Remote Sensing, School of Earth Sciences, Bharathidasan University, Tiruchirappalli - 620 023, IN

Abstract

The present paper narrates how various types of geological. structural, geomorphological and subsurface geological data have been collected and integrated in various pennutations and combinations to select suitable mechanisms for artificial recharge in Ayyar basin of Tamil Nadu.

Keywords

Remote Sensing, Artificial recharge, Groundwater, Tamil Nadu.

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S. Anbazhagan ¹, R. Venkatachalapathy ¹, R. Neelakantan ¹

Affiliations
1 Department of Geology, Periyar University, Salem-636011, IN

Abstract

No Abstract.

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S. Anbazhagan ¹, S. M. Ramasamy ²

Affiliations
1 Department of Earth Sciences, Indian Institute of Technology Bombay, Powai, Mumbai - 400 076, IN
2 Centre for Remote Sensing, Bharathidasan University, Tiruchirappalli-620 023, IN

Abstract

The paper presents an approach of selecting suitable areas for artificial groundwater recharge structures through remote sensing and integrated terrain analysis by statistical method. The study area "Ayyar basin" is an aquifer system of gneisses and charnockite litho-Units in Tiruchirappalli district, Tamil Nadu. The area has witnessed a steady decline in groundwater table. Hence, the scope for artificial groundwater recharge was studied in this area with the help of Tamil Nadu State Council for Science and Technology (TNSCS&T). Numerical database was generated for water level, lineament density, slope, drainage density, soil type, thickness of soil, thickness of weathered zone, thickness of fractured zone and depth to bedrock through remote sensing and hydrogeological study. These nine variables were used as input parameters and factor analysis with varimax rotation was carried out. In the factor analysis, the factor in which the variables loaded significantly were considered for further analysis. Using factor scores, the domains where the water level was deeper with decrease of slope and drainage density, and increase of lineament density, thickness of soil, thickness of weathered zone, thickness of fractured zone and depth to bedrock were buffered out. The buffered domains derived from each factor were finally integrated and thus the entire study area was fragmented into a number of domains suitable for artificial recharge. Such statistical output has not only helped to identify the suitable locations for artificial recharge but also indicated their controlling terrain characteristics, thus leading to the prioritization of area for specific method of cost effective recharge.

Keywords

Artificial Recharge, Remote Sensing, Lineaments, Statistical Modeling, Ayyar Basin, Tamil Nadu.

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S. Anbazhagan ¹, R. Neelakantan ¹, S. Arivazhagan ¹, G. Vanaraju ¹

Affiliations
1 Department of Geology, Periyar University, Salem-636011, IN

Abstract

Kolli hills is one of the small tourist spots in Tamil Nadu falls in Eastern Ghats. In general, improper land management, intensive weathering and rainfall trigger frequent slope failures and landslide in hill systems. During December 2005, similar slope failure had occuued in the Southeastern part of Kolli hills. Fractures and open cracks had developed along with horizontal and vertical displacements. Continuous heavy rainfall deforestation agricultural practices and obstruction of natural flow are observed to be the causative factors for such failure. Field investigations and other parameters were studied in detail. Abandoning of current agriculture practices and permitting natural flow of the streams are immediate steps recommended for stabilising such vulnerable slope.

Keywords

Fractures, Land Subsidence Landslide, Kolli Hills, Tamil Nadu.

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S. Anbazhagan ¹

Affiliations
1 Department of Electrical Engineering, Annamalai University, IN

Abstract

The objective in ground resistance is to attain the most minimal ground safety esteem conceivable that bodes well monetarily and physically. An application of artificial neural networks (ANN) to presage and relegation has been growing rapidly due to sundry unique characteristics of ANN models. A decent forecast is able to capture the dubiousness associated with those ground resistance. A portion of the key instabilities are soil composition, moisture content, temperature, ground electrodes and spacing of the electrodes. Propelled by this need, this paper endeavors to develop a generalized regression neural network (GRNN) to predict the ground resistance. The GRNN has a single design parameter and expeditious learning and efficacious modeling for nonlinear time series. The precision of the forecast is applied to the Athens seasonal variation of ground resistance that shows the efficacy of the proposed approach.

Keywords

Ground Resistance, Generalized Regression Neural Network, Forecasting.

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K. S. Sajinkumar ¹, S. Anbazhagan ², A. P. Pradeepkumar ³, V. R. Rani ⁴

Affiliations
1 Geological Survey of India, Kerala Unit Manikanteswaram P O, Trivandrum - 695 013, IN
2 Department of Geology, Periyar University, Salem - 636 011, IN
3 Department of Geology, University College, Trivandrum - 695 034, IN
4 Central Groundwater Board, Trivandrum - 695 004, IN

Abstract

The climatic condition of Western Ghats has influenced the process of weathering and landslides in this mountainous tract along the southwest coast of India. During the monsoon period, landslides are a common in the Western Ghats, and its intensity depends upon the thickness of the loose unconsolidated soil formed by the process of weathering. Debris landslides with a combination of saprock, saprolite and soil, indicate the role of weathering in landslide occurrences. This paper reports on how the weathering in the windward slope of Western Ghats influences the occurrence of landslides and the factors which accelerate the weathering process. Rock and soil samples were collected from the weathering profile of hornblende gniess and granite gneiss. The chemical analysis and the calculated Chemical Index of Alteration (CIA) indicate the significant weathering and its possible influence on landslide occurrences in the study area. Mainly, the CIA value of lateritic soil and forest loam indicated the extent of high chemical weathering in this region. Rainfall is the dominant parameter influencing the chemical weathering process. In addition, deforestation, land use practices and soil erosion are some of the other important factors accelerating the weathering process and landslide occurrences in the region. The locations of the previous landslides superimposed on geology and soil show that most of the landslide occurrences are associated with the highly weathered zone, particularly lateritic soil and the 'severe' (rock outcrop) erodability zone.

Keywords

Weathering, Landslides, CIA, Western Ghats, Kerala.

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S. Anbazhagan ¹, N. Kumarappan ¹

Affiliations
1 Department of Electrical Engineering, Annamalai University, IN

Abstract

There is a general consensus that the movement of electricity price is crucial for electricity market. The binary electricity price classification method is as an alternative to numerical electricity price forecasting due to high forecasting errors in various approaches. This paper proposes a binary classification of day-ahead electricity prices that could be realized using discrete cosine transforms (DCT) based neural network (NN) approach (DCT-NN). These electricity price classifications are important because all market participants do not to know the exact value of future prices in their decision-making process. In this paper, classifications of electricity market prices with respect to pre-specified electricity price threshold are used. In this proposed approach, all time series (historical price series) are transformed from time domain to frequency domain using DCT. These discriminative spectral co-efficient forms the set of input features and are classified using NN. The binary classification NN and the proposed DCT-NN were developed and compared to check the performance. The simulation results show that the proposed method provides a better and efficient method for day-ahead deregulated electricity market of mainland Spain.

Keywords

Price Forecasting, Discrete Cosine Transforms, Neural Network, Binary Electricity Price Classification, Electricity Market.

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R. Vijayalakshmi ¹, S. Anbazhagan ²

Affiliations
1 Department of Pharmaceutical Analysis, Gautham College of Pharmacy (Affiliated to Rajiv Gandhi University of Health Sciences), R.T. Nagar post, Bangalore, Karnataka-560032, IN
2 Karuna College of Pharmacy (Affiliated to Kerala University of Health Sciences), Irringuttur, Thirumittacode P.O. Palakad Dist. Kerala-679533, IN

Abstract

A simple, selective, rapid and precise RP-HPLC has been developed for the simultaneous estimation of Metamizole sodium and Pitofenone HCl in presence of Fenpiverinium bromide of combined Pharmaceutical dosage forms. The Inertsil ODS 3V C-18 Column was used for Metamizole sodium and Pitofenone HCL separation. The Mobile phase used was sodium dihydrogen phosphate buffer : Methanol,(0.05M, pH 5.0), (53:47 v/v) at a flow rate of 1ml/min at 286nm. The calibration curves was linear at a concentration range of 700.90 μg/ml to 1301.73 μg/ml and 7.01 μg/ml to 13.01 μg/ml with its regression coefficient (r²=0.9992 and 0.9995) for Metamizole sodium and Pitofenone HCl respectively was obtained. The LOD and LOQ was found to be in the range of 21.6174 μg/ml and 65.507 μg/ml for Metamizole sodium and 0.1701 μg/ml and 0.5155 μg/ml for Pitofenone HCl respectively. The method is highly sensitive and successfully applied for determination of Metamizole sodium and Fenpiverinium bromide in tablet dosage forms.

Keywords

Metamizole Sodium, Pitofenone HCl, Reverse Phase High Performance Liquid Chromatography.

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S. Anbazhagan ¹, V. Sivakumar ²

Affiliations
1 Department of Electrical Engineering, Annamalai University, IN
2 Anubavam Technologies Private Limited, US

Abstract

Electricity price forecasting is a challenging problem owing to the very great volatility of price which depends on many factors. This is especially prominent for both producers and consumers where a versatile price forecasting is crucial. This paper contributes an extreme learning machine (ELM) to classify the prices. These price classifications are essential since all market players do not know the precise value of future prices in their deciding procedure. In this paper, bi-classification model is proposed for prices utilizing the pre-specified price threshold. Three alternative classification models based on neural networks (NNs) are also proposed in bi-classification of prices. The performance of the proposed models is compared in terms of classification error and accuracy. The simulation results show that the ELM classification model is superior compared to three other classification models based on NNs. The performances of our models are evaluated using real data from vertically unbundled mainland Spain power system market.

Keywords

Electricity Price Classification, Extreme Learning Machines (ELM), Power System Market, Price Forecasting.

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G. Kavitha ¹, A. Jothibasu ¹, S. Anbazhagan ¹

Affiliations
1 Centre for Geoinformatics and Planetary Studies, Periyar University, Salem-636011, Tamil Nadu, IN

Abstract

The curvature of river profiles has long been taken to be a fundamental indicator of the underlying processes governing fluvial erosion and thereby of landscape evolution. Longitudinal profile is a graph of distance verses elevation is an x-y plot showing bed elevation as a function of downstream distance. Due to the plate movement is considerably slow, the human history record is too short to register landscape change for such a long time scale. In the present study, an attempt has been made the quantitative analysis of geomorphic indices coupled with some mathematical models for the Thoppaiyar sub-basin and its 16 micro-basins, including the gradient index (SL), normalized gradient index (SL/k), Profile complexity index (PCI) and slope-area relationship (Slr). Based on quantitative results of these geomorphology indices, this study suggests that the important factor influencing landscape of the Thoppaiyar sub-basin. Topographic map, IRS P6 LISS III satellite data, 10 m contour interval, SRTM data and ArcGIS 9.3 software were utilized. The contour lines of topographic maps of the main river and 14 micro basins are digitized as control points. Models of the longitudinal profiles using simple mathematical functions were made considering four functions for describing the form of longitudinal profiles. The abnormally high SL and SL/k values indicated that a decreasing trend from lower to mid-stream areas and the result of slope–area relationship also indicated that the regression line of the upper and lower steam exhibit an obvious right-shift could be explained by geodynamic models of active deformation in Thoppaiyar sub-basin.

Keywords

River Profile, Gradient Index, Mid-Stream, Fluvial Geomorphology, Thoppaiyar.

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A. Jothibasu ¹, S. Anbazhagan ¹

Affiliations
1 Centre for Geoinformatics and Planetary Studies, Periyar University, Salem-636011, Tamil Nadu, IN

Abstract

Due to the increase of water demand and threatening climate change, in the recent years have witnessed much focus on global drought scenarios. In India, Tamil Nadu have deficient rainfall (921 mm) compared to national average of (1200 mm) leading to over-reliance on irrigation for agriculture, and increasing extraction of groundwater reserves (estimates show over 60 percent of reserves are already exhausted) pose a big issue for the future and its indicate the continuing susceptibility of the society to drought. This study demonstrates the cumulative drought hazard assessment using climatic, biophysical and social factors in the Ponnaiyar River basin, Tamil Nadu, India. It was hypothesized that the key climatic, biophysical and social factors that define meteorological drought hazard, it is rainfall, normalized deviation of rainfall whereas for agricultural drought hazard were soils, geomorphology, drainage density, land use, and relief, and for hydrological drought hazard, it is lithology, depth to water table, and surface water bodies. The construction for the derivation of an agricultural, meteorological, and hydrological drought hazard map was created through the development of a numerical weighting scheme to evaluate the drought potential of the classes within each factor. A cumulative map created through spatial join of all the three types of drought provided a drought hazard scenario in totality. The area with different severity of drought hazards under cumulative drought hazards scenario is about 40% of the area under high to very high drought scenario. It is revealed the immediate attention have made to groundwater development for sustainable environment in the study area.

Keywords

Remote Sensing, GIS, Normalized Weight, Drought Hazard, Southern India.

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S. Anbazhagan ¹, C. R. Paramasivam ¹

Affiliations
1 Centre for Geoinformatics and Planetary Studies, Department of Geology, Periyar University, Salem, IN

Abstract

The land use and land cover information in the form of maps and statistical derived data has dealt with spatial planning, management and utilization of land. Digital image processing techniques can explore the changes of land use within the specified time period. Change detection technique used to determine land use and land cover changes in the magnesite mining area situated in Salem in different time periods from 1992 to 2010. The Landsat TM multi-temporal satellite data for the year 1992, 2001 and 2010 were utilized in the change detection analysis. Supervised classification method was employed using the maximum likelihood procedure in ENVI 4.7 software. The land use and land cover changes were observed through spatial data classification, statistical operations through relative changes, change matrix and accuracy assessment. The findings clearly depict that the changes in land use and land cover are due to mining activity in the study area. The results obtained in this study for the stipulated year’s shows that the increase in the mining area +2.35 sq.km affects the net change value of area of scrub forest by +10.77 sq.km and built-up land by +24.21 sq.km, hence reducing the dense forest by -12.98 sq.km and agricultural land by -24.34 sq.km area respectively. The relative changes are assessed with the aid of the raster image in pixels and the net change domain pixels evaluation for the period of 1992 - 2010. Error matrixes are function of cross tabulation and assess the classification accuracy. The current study reveals an overall accuracy of 78.82%.

Keywords

Change Detection, Land Use and Land Cover, Change Matrix, Magnesite Mines-Salem.

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S. Anbazhagan ¹

Affiliations
1 Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, IN

Abstract

The dental and skeletal relationships in the head are studied in Cephalometric analysis. This research work addresses Bjork’s analysis for the classification of patients. In this research work, the backpropagation neural network (BPNN), and generalized regression neural network (GRNN) classifiers are used and studied for the diagnosis of Cephalometric analysis. In this study, a total of 304 (male 109, female 195) patient’s case records were collected for this study. All the collected clinical data are used for classification. For training and testing the proposed models, patients' data were separated by four-fold cross-validation. Based on Bjork analysis, experimental results show that GRNN provided achieving the performance of 97.39% of good classification results when compared to the BPNN model. The GRNN approach is feasible and was found to be achieving a performance of 97.39% of the correct detection of patients.

Keywords

Bjork’s Analysis, Cephalometric Analysis, Back Propagation Neural Network, Generalized Regression Neural Network.

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S. Anbazhagan ¹

Affiliations
1 Department of Electrical Engineering, Annamalai University, IN

Abstract

This paper proposes a Teaching learning-based optimization (TLBO) algorithm for the multilevel image thresholding using Kapur entropy. In image processing, the thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. However, they are computationally expensive reaching out to multilevel thresholding since they thoroughly search the optimal thresholds to enhance the fitness functions. In order to validate the chaotic characteristic of multilevel thresholding, a TLBO algorithm is modeled. The proposed model is an algorithm-specific, parameterless algorithm that does not require any algorithm-specific parameters to be controlled by maximizing the Kapur entropy of various classes for image thresholding. The proposed model is compared with recent algorithms to threshold the seven standard benchmark and three test images. The simulation results have higher fitness function values even with the increase of the threshold number with less computation time. The Jaccard measure values are close to 0.99.

Keywords

Kapur’s Entropy, Multilevel Thresholding, Teaching Learning Based Optimization.

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