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4篇 您的检索式:作者名="Khanindra Pathak"
    题名 作者 年代 出处 被引量
1Superhydrophobie and olephilic barium sulfate material for oil spill clean- ups: Fabrication of surface modified sorbent by a one-step interaction approach 显示文摘Manoj Patowary Rajakumar Ananthakrishnan Khanindra Pathak 2014Journal of environmental Chemical Engineering2014,,2:1
2Chmieal modification of hygroscopic magnesimn carbonate into superhydrophobic and oleophilic sorbent suitable for removal of oil spill in water 显示文摘Manoj Patowary Rajakumar Ananthakrishnan Khanindra Pathak 2014Applied surface Science2014,,30:1
3Superhydrophobie and oleophilic barium sulfate material for oil spill clean-ups显示文摘Manoj Patowary Rajakumar Ananthakrishnan Khanindra Pathak 2014Journal of Environmental Chemical Engineering2014,2,4:1
4Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk(FFR)mapping in Melghat Tiger Reserve(MTR)forest显示文摘A comparative study of Frequency Ratio(FR)and Analytic Hierarchy Process(AHP)models are performed for forest fire risk(FFR)mapping in Melghat Tiger Reserve forest,central India.Identification of FFR depends on various hydrometeorological parameters altitude,slope,aspect,topographic position index,normalized differential vegetation index,rainfall,air temperature,land surface temperature,wind speed,distance to settlements,and distance by road are integrated using a GIS platform.The results from FR and AHP show similar trends.The FR model was significantly higher accurate(overall accuracy of 81.3%,kappa statistic 0.78)than the AHP model(overall accuracy 79.3%,kappa statistic 0.75).The FR model total forest fire risk areas were classified into five classes:very low(7.1%),low(22.2%),moderate(32.3%),high(26.9%),and very high(11.5%).The AHP fire risk classes were very low(6.7%),low(21.7%),moderate(34.0%),high(26.7%),and very high(10.9%).Sensitivity analyses were performed for AHP and FR models.The results of the two different models are compared and justified concerning the forest fire sample points(Forest Survey of India)and burn images(2010-2016).These results help in designing more effective fire management plans to improve the allocation of resources across a landscape framework.Narayan Kayet Abhisek Chakrabarty Khanindra Pathak Satiprasad Sahoo Tanmoy Dutta Bijoy Krishna Hatai 2020Journal of Forestry Research2020,31,2:1
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