Assessment of long term agricultural drought in Tamilnadu, India using NDVI anomaly

Vaani N.* and Porchelvan P.

The study of long term agricultural drought becomes essential for monsoon dependent countries like India in order to identify the drought prone areas and develop various strategies to improve the agricultural productivity in the country. The present study attempted this by assessing the long term agricultural drought for a period of 20 years from 1984 to 2003 for the State of Tamil Nadu, India using one of the popular indexes called Normalized Differenced Vegetation Index (NDVI) anomaly. The NDVI from Global Inventory Modeling and Mapping Studies (GIMMS) which is an open source data, has been used in the present study to compute the NDVI anomaly. The agricultural drought intensity maps prepared using NDVI anomaly will help the state government authorities to identify the drought risk areas and prioritize their actions based on the severity of the drought level.

The result showed that the State experienced moderate to severe drought situation during most of the years in the analysis period of 1984 to 2003. The drought affected area and its percentage in the entire state during the study period confer the fact that the current drought condition of the state could have been foreseen two decades back and hence enabled the agrarian society to be precautionary. The recurrent drought in the state necessitates the Government to take suitable preventive measures to avoid drought in future.

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Effect of sinuosity on flood disaster: A study of Kadvi river channel, Maharashtra

Chougale Sujit S. and Sapkale Jagdish B.*

The present research work evaluated the variation in sinuosity index, terrain, morphometry, relief of Kadvi river channel of Kolhapur, Maharashtra as a result of human and natural interference. Study area forms a part of Kadvi river basin. The confluence of Warna river and Kadvi river is located near Thergaon village. Kadvi river is a main tributary of Warna river. Kadvi river basin consist of an area about 428.81 sq.km and includes sub tributaries such as Potphogi river, Ambardi river and Shali river. The various channel patterns of river i.e. straight, sinuous, meandering and braided have been observed in the channel segments of Kadvi.

The sinuosity index for Kadvi and its tributaries varies from 1.07 to 2.04. The average sinuosity values of Kadvi river, Potphogi river, Ambardi river, Shali river are 1.77, 1.32, 1.29 and 1.52 respectively. The variation in sinuosity index is caused due to fluvial processes, slope of basin, water discharge, deforestation as well as human interference. All these collectively have an influence on flood catastrophe in the study area. Geomorphic characteristic and within channel variations are also influenced by the variability in river basin and its hydrology.

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A novel image processing approach for finding the bubble count in neutron dosimeter

Thamotharan B.*, Vaithiyanathan V., Venkatraman B., Anishin Raj M.M., Karthikeyan B. and Venkata Keerthy S.

The bubble detector is used to detect the amount of neutron and hence used to measure the neutron dose under intense gamma field. It can be used to monitor radioactive exposure. The number of nucleated bubbles yields the neutron dose. Hence the accuracy of the measurement depends on the counting of bubbles. This work proposes an image processing based technique to increase the accuracy in finding the number of bubbles in the bubble detector.

It is carried out by using watershed and region growing based segmentation. The image processing segmentation techniques shade the digital image of bubble detector into various segments from which the number of bubbles in the bubble detector is detected.

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Compressive behaviour of steel fibre reinforced concrete columns heated up to 8000C

K. Ratna Tej Reddy* and K. Srinivasa Rao

The present study is aimed to determine the performance of steel fiber reinforced RC columns at high temperatures which are cooled by different methods. Thirty-three specimens of 1200 mm in length and 150 x 150 mm in cross section were cast and heated to the temperatures ranging from 100 to 800OC which were exposed for duration of an hour and cooled by air cooling and water quenching. The compressive strength is determined with the help of a universal testing machine and the percentage variations among steel fiber reinforced concrete and ordinary concrete are plotted.

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Estimation of Vibration Parameters based on Rock Mass Rating (RMR) for Blast in Rocky Sites

Kumar Ranjan*, Yerunkar Pravin Prakash and Bhargava Kapilesh

Quarrying, mining and construction activity pose a serious problem to environment considering the ground vibrations, air blasts and fly rocks generated due to the blasting involved in these activities. If the blasting is not planned well, then it may lead to some serious damage to life and property. As the countries are developing and cities expanding, there is hardly any distance left between the settlements and quarries/ mines.

Therefore, an impact assessment of the blast induced ground vibrations is now an important pre-requisite for future operations of quarries/mines. In the recent past, the topic of blast loads on structures has received considerable attention of researchers. Site specific empirical models for blast induced vibration parameters like Peak Particle Velocity (PPV), Peak ground acceleration (PGA) and Peak Particle Displacement (PPD) are commonly used for blast resistant designs.

However, these empirical models are not able to consider the variation in the rock properties such as UCS, RQD, discontinuities in rocks and its conditions and water table location etc. Hence, in this study, a total of 156 blast data from various sites have been collected and used to propose a generalized empirical model to estimate PPV in terms of RMR. Standard errors and coefficients of correlation for prediction of blast induced vibration parameters by various empirical models are obtained with respect to the observed soil field data. The present empirical model has been compared with the models of other researchers and was found in good agreement. The present model, having maximum coefficient of correlation, can be directly used in calculation of PPV. In the absence of field blast vibration data, the present model will be very useful to evaluate blast vibration parameter by using only basic rock property specified in terms of RMR.

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Tsunami and its after effects on the habitation

Saleem Sehba

On 26th December 2004, simultaneous occurrence of two catastrophe beneath and above the Indian Ocean altered the topography of the land which existed in the adjacent to South Asia with the closest proximity of that being the southwest shores of northern Indonesia. The first catastrophe, the primary one, was the earthquake that resulted from an extensive rupture along the Sumatra-Andaman fault which was also the precipitating event. This was followed by tsunami, the secondary event that resulted from earthquake and its underwater landslides. The earthquake occurred along the fault in the subduction zone where the Indian tectonic plate is going below the overriding Burmese plate.

As a result, the ocean floor broke and there was a vertical displacement of about 15 to 20 meters which ranged around 1600 kilometers along the fault causing a large displacement of water thereby generating tsunami waves. The mega thrust earthquake was unusually large in geographical and geological extent. Seismographic and acoustic data indicate that the first phase involved a rupture of 400 kilometers long and 100 kilometers wide located 30 kilometers beneath the seabed. This is the largest rupture ever known to have been caused by an earthquake. The earthquake generated a seismic oscillation of the Earth's surface up to 20 to 30 centimeters which is equivalent to the effect of the tidal forces caused by sun and moon. The energy released by the earthquake on the surface of the earth and ocean was estimated at 10 million joules or 26 megatons of TNT or 1500 Hiroshima bombs.

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A Comparative Analysis of Rainfall Prediction Models using Artificial Neural Networks

Renuga Devi S.

This paper presents a comparative analysis of the performance of different neural network models designed for rainfall prediction. In the course of this research, many training algorithms, input models network architectures, input data sets, methods to choose data for training and testing were employed. This led to the discovery of shortcomings in few models and methods to improve these models or alternatives for these models.

In addition, Wavelet Neural Network based rainfall prediction model was used to check for any improvement in the performance of the prediction accuracy. As a result of the analysis of myriad combinations of the above said aspects, a better performing Wavelet ANN model for the prediction of rainfall, using segregation of input data set is found to be in good prediction accuracy.

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