Institution
Indian Institute of Technology Indore
Education•Indore, Madhya Pradesh, India•
About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Fading & Support vector machine. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.
Topics: Fading, Support vector machine, Raman spectroscopy, Band gap, Thin film
Papers published on a yearly basis
Papers
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TL;DR: In this article, the effect of soil moisture on the performance of conventional meteorological thresholds was investigated using a probabilistic approach, and the results showed that when the antecedent moisture content in soil is less, only severe rainfall events can trigger landslides in the study area; while less severe rain events can also trigger landslide when the soil is wet.
Abstract: Landslides triggered by heavy rains are increasing in number and creating severe losses in hilly regions across the world. Rainfall thresholds on regional and local-scales are being used for forecasting such events, for efficient early warning. Empirical and probabilistic approaches for defining rainfall thresholds are traditional tools which are being used as part of the forecasting system for rainfall induced landslides. Such methods are easy-to-use and are based on statistical analyses. They can be derived without looking into the complex hydro-geological processes involved in slope failures, but are often associated with the disadvantage of higher false alarms, limiting their applications in a regional landslide early warning system (LEWS). This study is an attempt to improve the performance of conventional meteorological thresholds by considering the effect of soil moisture, using a probabilistic approach. Idukki district in southern part of India is highly susceptible to landslides and has witnessed major socio-economical setbacks in the recent disasters happened in 2018 and 2019. This tourist hub is now in need of a landslide forecasting system, which can help in landslide risk reduction. This study attempts to understand the effect of averaged soil moisture estimates derived from passive microwave remote sensing data, for improving the performance of conventional empirical and probabilistic thresholds. For defining empirical thresholds, an algorithm-based approach such as Calculation of Thresholds for Rainfall-induced Landslides Tool (CTRL-T) has been used. Probabilistic thresholds were defined using a Bayesian approach, finding the posterior probability of occurrence using the marginal and conditional probabilities of the control parameters along with the prior probability of occurrence of landslide. The derived rainfall thresholds were quantitatively compared with the Bayesian probabilistic threshold derived using rainfall severity and soil wetness using an area under the curve (AUC) based on receiver operating characteristics (ROC) curve method. The results show that when the antecedent moisture content in soil is less, only severe rainfall events can trigger landslides in the study area; while less severe rainfall events can also trigger landslides when the soil is wet. The role of soil wetness in the initiation is used to improve the performance of the conventional methods, and a ROC approach was used for the statistical comparison of different models. Further, the results indicated that the probabilistic threshold using rainfall severity and soil wetness outperformed the conventional approaches with AUC of 0.96, being the most sensitive and specific among the models considered. This result opens new promising perspectives for the development of an operational LEWS in the Idukki district based on a combination of rainfall and soil moisture data. Moreover, this work contributes to strengthen the advancing trend of hydro-meteorological thresholds based on soil moisture, which is gaining a growing attention in landslide studies and that, to date, was lacking evidences in monsoon regions.
31 citations
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21 Sep 2020TL;DR: In this article, the CaTiO3 pyramids were applied as an electrode modifier to develop an electrochemical sensor to determine urea using the cyclic voltammetry and I-V techniques.
Abstract: Herein, we report novel CaTiO3 pyramids, prepared by a hydrothermal approach using calcium nitrate and titanium butoxide in the presence of sodium hydroxide. The physio-chemical properties of the synthesized CaTiO3 pyramids were probed by PXRD, FTIR, BET, SEM and EDX. The synthesized CaTiO3 pyramids possess a good specific surface area which is beneficial for electrochemical applications. Thus, the CaTiO3 pyramids were applied as an electrode modifier to develop an electrochemical sensor. The working surface area of the glassy carbon (GC) electrode was fabricated with CaTiO3 pyramids using the drop casting method. This fabricated electrode was employed to determine urea using the cyclic voltammetry and I–V techniques. The fabricated electrode exhibited a good detection limit of 1.6 μM.
31 citations
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TL;DR: In this paper, a new experimental technique is designed to measure the mesh stiffness of a given gear pair, which is important for understanding the dynamics of gearboxes, and the results of the experiment are found having a good match with that obtained from the FE method.
31 citations
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TL;DR: In this paper, a comprehensive characterization of different industrial ashes is necessary to clearly define the chemical characteristics that have influencing role on the lime-based fly ash bricks, including their mineralogy, particle morphology, and lime reactivity.
31 citations
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TL;DR: The reaction kinetics of metal hydride pairs consisting of La09Ce01Ni5, La08Ce02Ni5 and LaNi47Al03 were measured at different temperatures to determine their suitability for MHCSs.
31 citations
Authors
Showing all 1738 results
Name | H-index | Papers | Citations |
---|---|---|---|
Raghunath Sahoo | 106 | 556 | 37588 |
Biswajeet Pradhan | 98 | 735 | 32900 |
A. Kumar | 96 | 505 | 33973 |
Franco Meddi | 84 | 476 | 24084 |
Manish Sharma | 82 | 1407 | 33361 |
Anindya Roy | 59 | 301 | 14306 |
Krishna R. Reddy | 58 | 400 | 11076 |
Sudipan De | 54 | 99 | 10774 |
Sudip Chakraborty | 51 | 343 | 9319 |
Shaikh M. Mobin | 51 | 515 | 11467 |
Ashok Kumar | 50 | 405 | 10001 |
Ankhi Roy | 49 | 259 | 8634 |
Aditya Nath Mishra | 49 | 139 | 7607 |
Ram Bilas Pachori | 48 | 182 | 8140 |
Pragati Sahoo | 47 | 133 | 6535 |