Refine your search
Collections
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kushwaha, Daniel Prakash
- Assessment of Meteorological Drought for Parbhani District of Maharashtra, India
Abstract Views :201 |
PDF Views:0
Authors
Affiliations
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
Source
International Journal of Agricultural Engineering, Vol 10, No 2 (2017), Pagination: 260-267Abstract
A study was carried out to estimate the drought occurrences for rainfed area of Parbhani district of Maharashtra, India. Rainfall plays an important role during crop growth in rainfed agriculture system. Rainfall data of 32 years (1983 - 2014) have been analyzed on annual, seasonal, monthly and weekly basis to find out drought occurrences at Parbhani. The drought analysis indicated that during the study duration the drought, normal and wet years were found to be 9.37, 68.75 and 21.87 per cent, respectively. The occurrences of drought, normal and wet seasons were 7.29, 73.95 and 18.75 per cent, respectively. The percentage of drought, normal and wet months were observed to be 48.43, 38.80 and 12.76 per cent, respectively while drought, normal and wet weeks were observed with a frequency of 70.07, 16.28 and 13.64 per cent, respectively. The research revealed that 9 years showed moderate drought intensity, 9 years showed mild drought intensity while the remaining 14 years observed with no drought condition. No severe or extreme drought was observed during this study duration. The mean value, standard deviation and coefficient of variation of annual rainfall were found to be 947.5 mm, 312.3 mm and 32.96 %, respectively. The analysis also indicated the need of assured irrigation during late winter and summer season.Keywords
Rainfall Analysis, Meteorological Drought, Drought Year.
References
- Bhelawe, Sanjay, Chaudhary, J.L., Manikandan, N. and Deshmukh, Rupesh (2015). Meteorological drought assessment in Raipur district of Chhattisgarh state, India. Plant Arch., 15 (1) : 465-469.
- Dabral, P.P. (1996). Meteorology drought analysis based on rainfall data. Indian J. Soil Cons., 24(1): 37-40.
- Dhar, O.N., Rakhecha, P.R. and Kolkarni, A.K. (1979). Rainfall study of severe drought year of India. Internat. Symposium Hydrological Aspect of drought, 1 : 28-36.
- India Meteorological Department (IMD) (1971). Climate Diagnostic Bulletin of India- June, July, August 1971; Rep. No 88, 89 and 90, National Climate Center, IMD. Pune.
- Kumar, D. and Kumar, S. (1989). Drought analysis based on rainfall data. Indian J. Soil Cons., 17 (1): 55-59.
- Manikandan, M. and Tamilmani, D. (2011). Assessment of occurrence and frequency of drought using rainfall data in Coimbatore, India. Asian J. Environ. Sci., 6 (2) : 136-146.
- Marathe, R.A., Mohanty, S. and Singh, S. (2001). Meteorological drought analysis based on rainfall data of Nagpur. J. Soil & Water Cons., 45 : 1-5.
- Ramdas, L.A. and Malik, A.K. (1948). Agricultural situation in India, Technical Bulletin, ICAR, New Delhi.
- Ray, C.R., Senpati, P.C. and Lal, R. (1987). Investigation of drought from rainfall data at Gopalpur, Orissa. Indian J. Soil Cons., 15 (1): 15-19.
- Ray, Lala I.P., Bora, P.K., Ram, V., Singh, A.K., Singh, R., and Feroze, S.M. (2012). Meteorological drought assessment in Barapani, Meghalaya. J. Indian Water Resour. Soc., 32 (1-2) : 56-61.
- Ray, Lala I.P., Bora, P.K., Singh, A.K., Singh, Ram, Singh, N.J. and Feroze, S.M. (2014). Impact and assessment of meteorological drought on rice based farming system in east Garo Hills District of Meghalaya, India. J. Agri. Search, 1(4): 227-232.
- Salas, J.D. (1986). State of the art of statistical technique for describing drought characteristic WARRDCC, International Seminar on Drought Analysis. Italy.
- Sharma, H.C., Chauhan, B.S. and Ram, S. (1979a). Probability analysis of rainfall for crop planning. J. Agril. Engg., 16 (3):22-28.
- Shrivastava, K. Salil, Raveendra, K. Raj and Pandey, Ashish (2008). Assessment of meteorological droughts in North Lakhimpur district of Assam. J. Indian Water Resour. Soc., 28(2): 26-31.
- Singh, Bhim, Arya, C.K., Singh, Jitendra and Mourya, K.K. (2014). Analysis of rainfall data for storage and irrigation planning in humid south-eastern plain of Rajasthan in India. J. Appl. & Natural Sci., 6 (1): 214-219.
- Singh, B. and Sharma, M.K. (2003). A study of pattern of rainfall of crop research station-Masodha(U.P.). Mausam, 54 (2) : 552-556.
- Sonakar, S.V., Kumar, Vinod and Tomar, A.S. (2016). Drought analysis for Udham Singh Nagar district of Uttarakhand (India). Internat. J. Res. Engg. & Appl. Sci., 6 (4) : 29-37.
- Tiwari, K.N., Paul, D.K. and Gontia N.K. (2007). Characterization of meteorological drought. Hydrology, 30 (1-2): 15-27.
- Modeling Suspended Sediment Concentration Using Multilayer Feedforward Artificial Neural Network at the Outlet of the Watershed
Abstract Views :178 |
PDF Views:0
Authors
Affiliations
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
Source
International Journal of Agricultural Engineering, Vol 10, No 2 (2017), Pagination: 302-313Abstract
Eight multilayer feedforward artificial neural network based models were developed to predict daily suspended sediment concentration for the Baitarani river at Anandpur gauging station using daily discharge and daily suspended sediment concentration. The 30 years data (June 1977 to September 2006) used in this study was divided into two sets viz. a training set (1977-1996) and a testing set (1997-2006). Artificial neural networks (ANN) models were calibrated by using multilayer feedforward back propagation neural networks with sigmoid activation function and Levenberg-Marquardt (L-M) learning algorithm. The performance of the developed models was evaluated qualitatively and quantitatively. In qualitative evaluation of models, the observed and the computed suspended sediment concentration were compared using sediment hydrographs and scatter plots during testing period. Akaike’s information criterion (AIC), correlation co-efficient (r), mean square error (MSE), ischolar_main mean square error (RMSE), minimum description length (MDL), co-efficient of efficiency (CE) and normalized mean square error (NMSE) indices were used for quantitative performance evaluation of the models. Results on the basis of qualitative and quantitative evaluation indicate that M-6 model with (7-5-5-1) network architecture is better than all models at Anandpur station and it was also found that artificial neural network based model is better than physics based models such as sediment rating curve and multiple linear regression.Keywords
Multilayer Feedforward Artificial Neural Networks, Levenberg-Marquardt (L-M) Learning Algorithm, Sigmoid Activation Function, Suspended Sediment Concentration Modeling, Sediment Rating Curve, Multiple Linear Regression.References
- ASCE (2000a). Task committee on application of artificial neural networks in hydrology, Artificial Neural Networks in Hydrology, I: Preliminary concepts. J. Hydrologic Engg. ASCE, 5(2) : 124-137.
- ASCE (2000b). Task committee on application of artificial neural networks in hydrology, Artificial Neural Networks in Hydrology, II: Hydrologic Application. J. Hydrologic Engg. ASCE, 5(2) : 115-123.
- Churchland, P. S. and Sejnowski, T. J. (1992).The computational brain. MA: MIT Press, Cambridge.
- Danh, N.T., Phien, H.N. and Gupta, A.D. (1999). Neural network models for river flow forecasting. Water S.A., 25 (1): 33-39.
- Dawson, C.W. and Wilby, R.L. (1998). An artificial neural network approach to rainfall runoff modeling. Hydrol. Sci. J., 43(1): 47-66.
- Eisazadeh, L.L., Sokouti, R., Homoaee, M. and Pazira, E. (2013). Modelling sediment yield using artificial neural network and multiple linear regression methods. Internat. J. Biosci., 3(9): 116-122.
- Elshorbagy, A., Simonovic, S.P. and Panu, U.S. (2000). Performance evaluation of artificial neural networks for runoff prediction. J. Hydroolic Engg., 5 (4): 424-427.
- Gharde, K.D., Kothari, M., Mittal, H.K., Singh, P.K. and Dahiphale, P.A. (2015). Sediment yield modelling of Kal river in Maharashtra Using Artificial Neural Network Model. Res. J. Recent. Sci., 4: 120-130.
- Ghorbani, M.A., Hosseini, S.H., Fazelifard, M.H. and Abbasi, H. (2013). Sediment load estimation by MLR, ANN, NF and sediment rating curve (SRC) in Rio Chama river. J. Civil Engg. & Urbanism, 3(4): 136-141.
- Imrie, C.E., Durucan, S. and Korre, A. (2000). River flow prediction using artificial neural networks: generalization beyond the calibration range. J. Hydrol., 233 (1-4): 138-153.
- Jain, S.K. (2001). Development of integrated sediment rating curves using ANN. J. Hydrologic Engg., ASCE, 127(1): 30–37.
- Jain, S. (2008). Development of integrated discharge and sediment rating relation using a compound neural network. J. Hydroolic Engg., 13(3): 124–131.
- Jie, L.C. and Yu, S.T. (2011). Suspended sediment load estimate using support vector machines in Kaoping River basin. 978-1-61284-459-6/11 2011 IEEE.
- Kermani, F., Shams-Ghahfarokhi, M., Gholami-Shabani, M., and Razzaghi-Abyaneh, M. (2016). Diversity, molecular phylogeny and fingerprint profiles of airborne Aspergillus species using random amplified polymorphic DNA. World J. Microbiol. Biotechnol., 32 (6) : 96
- Khoob, A.R. (2008). Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semiarid environment. Irrigation Sci., 27 (1): 35-39.
- Kisi, O. (2010). River suspended sediment concentration modeling using a neural differential evolution approach. J. Hydrol., 389 : 227–235.
- Kisi, O. and Shiri, J. (2012). River suspended sediment estimation by climatic variables implication: Comparative study among soft computing techniques. Computers & Geosciences, 43 : 73–82.
- Kisi, O., Dailr, A.H., Cimen, M. and Shiri, J. (2012). Suspended sediment modeling using genetic programming and soft computing techniques. J. Hydrol., 450– 451: 48–58.
- Kumar, A.R.S., Ojha, C.S.P., Goyal, M.K., Singh, R.D. and Swame, P.K. (2011). Modeling of suspended sediment concentration at Kasol in India using ANN, Fuzzy Logic, and decision tree algorithms. J. Hydrologic Engg., 17: 394-404.
- Kumar, D., Pandey, A., Sharma, N. and Flügel, W. (2016). Daily suspended sediment simulation using machine learning approach. Catena, 138 : 77–90.
- Kuo et al. (2011). A logical calculus of the ideas immanent in nervous activity. Bull. Mathematical Biophys., 5 : 115-133.
- Nayak, P.C., Sudheer, K.P., Rangan, D.M. and Ramasastri, K.S. (2004). A neurofuzzy computing technique for modelling hydrological time series. J. Hydrol., 291 : 52-66.
- Olyaie, E., Banejad, H., Chau, K.W. and Melesse, A.M. (2015). A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environ. Monitoring Assess., 187 : 189.
- Rajaee, T., Mirbagheri, S.A., Zounemat-Kermani, M. and Nourani, V. (2009). Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Sci. Total Environ., 407: 4916–4927
- Rai, R.K. and Mathur, B.S. (2008). Event- based sediment yield modeling using artificial neural network. Water Resour. Manage, 22: 423-441.
- Sarangi, A. and Bhattacharya, A.K. (2005). Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India. Agric. Water Mgmt., 78(3): 195–208.
- Shabani, M. and Shabani, N. (2012). Estimation of daily suspended sediment yield using artificial neural network and sediment rating curve in Kharestan watershed, Iran. Australian J. Basic & Appl. Sci., 6(11): 157-164.
- Shirsath, P.B. and Singh, A.K. (2010). A comparative study of daily pan evaporation estimation using ANN, regression and climate based models. Water Resour. Mgmt., 24 : 1571–1581.
- Singh, A., Imtiyaz, M., Isaac, R.K. and Denis, D.M. (2013). Comparison of artificial neural network models for sediment yield prediction at single gauging station of watershed in Eastern India. J. Hydrol. Engg., 18 :115-120.
- Land Surface Temperature Estimation Using Split Window Approach over US Nagar District of Uttarakhand State, India
Abstract Views :178 |
PDF Views:0
Authors
Affiliations
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Soil and Water Conservation Engineering, Gobind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
1 Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Soil and Water Conservation Engineering, Gobind Ballabh Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
Source
International Journal of Agricultural Engineering, Vol 10, No 2 (2017), Pagination: 354-359Abstract
To estimate land surface temperature (LST) has an important role for agriculture as well as global change of climate, growth of vegetation and glacier melting. It combines the results of all surface atmosphere interactions and energy fluxes between the surface and the atmosphere. Now-a-days, estimation of temperature of land surface is being calculated with the help of satellite images containing thermal infrared band. Though land surface temperature derived from satellite, could be a beneficial complement to conventional land surface temperature data sources. This research, proposed a methodology for determining land surface temperature through using a structured mathematical algorithm viz., split window (SW) algorithm. Split window algorithm has been used on LANDSAT 8 with operational land imager i.e. OLI sensor and thermal infrared sensor i.e. TIRS dataset of Udham Singh Nagar district. TIRS shows two thermal bands i.e. band 10 and band 11. SW approach requires brightness temperature value of both band 10 and band 11 as well as land surface emissivity which is calculated from OLI bands i.e. NIR and Red, for the estimation of land surface temperature. The spectral radiance was determined using thermal infrared bands i.e. band 10 and band 11. Emissivity was calculated by using normalized difference vegetation index i.e. NDVI threshold technique for which OLI bands 2, 3, 4 and 5 were utilized. SW approach uses brightness temperature of two bands of thermal infrared, mean and difference in land surface emissivity for estimating land surface temperature. In this paper, 6 Dec. 2015 date was selected as an example to show the approach of using SW technique to estimate the LST of Udham Singh Nagar district of Uttarakhand state in India.Keywords
Split Window Approach, Fractional Vegetation Cover, Land Surface Emissivity, Land Surface Temperature.References
- Cheung, C. and Hart, M. (2014). Climate change and thermal comfort in Hong Kong. Internat. J. Biometeorol., 58(2): 137–148.
- Cleveland, C. (2007). Heat Island Encyclopedia of Earth.
- Ema, K., Nengah, S. J. and Widiatmaka (2016). Satellite-based land surface temperature estimation of Bogor municipality, Indonesia. Indonesian J. Electrical Engg. & Computer Sci., DOI: 10.11591/ijeecs.v2.i1, 2 (1): 221-228.
- Gago, E.J., Roldan, J., Pacheco-Torres, R. and Ordonez, J. (2013). The city and urban heat islands: A review of strategies to mitigate adverse effects. Renewable & Sustain. Energy Reviews, 25(0): 749–758.
- Gosling, S., Lowe, J., McGregor, G., Pelling, M. and Malamud, B. (2009). Associations between elevated atmospheric temperature and human mortality: a critical review of the literature. Climatic Change, 92(3-4) : 299-341.
- Kleerekoper, L., Van-Esch, M. and Salcedo, T.B. (2012). How to make a city climate- proof, addressing the urban heat is land effect. Resour., Cons. & Recyc., 64(0): 30–38.
- Kolokotroni, M., Zhang, Y. and Watkins, R. (2007). The London Heat Island and building cooling design. Solar Energy, 81(1): 102–110.
- Kolokotroni, M., Ren, X., Davies, M. and Mavrogianni, A. (2012). London’s urban heat island: Impact on current and future energy consumption in office buildings. Energy & Buildings, 47(0) : 302–311.
- Kondo, H. and Kikegawa, Y. (2003). Temperature variation in the urban canopy with anthropogenic energy use. Pure & Appl. Geophys., 160(1–2) : 317–324.
- Li, Z.L., Wu, H., Wang, N., Qiu, S., Sobrino, J.A., Wan, Z., Tang, B.H. and Yan, G. (2013). Land surface emissivity retrieval from satellite data. Internat. J. Remote Sensing, 34 :3084-3127.
- Madlener, R. and Sunak, Y. (2011). Impacts of urbanization on urban structures and energy demand: What can we learn for urban energy planning and urbanization management. Sustain. Cities & Soc., 1(1): 45-53.
- Mirzaei, P.A. and Haghighat, F. (2010). Approaches to study Urban Heat Island, Abilities and limitations. Building & Environ., 45(10): 2192–2201.
- Oke, T.R. (1982). The energetic basis of the Urban Heat-Island. Quarterly J. Royal Meteorol. Soc., 108(455): 1–24.
- Quattrochi, D.A. and Luvall, J. C. (1999). Thermal infrared remote sensing for analysis of landscape ecological processes: Methods and applications. Landscape Ecol., 14(6): 577–598.
- Santamouris, M., Papanikolaou, N., Livada, I., Koronakis, I., Georgakis, C. and Argiriou, A. (2001). On the impact of urban climate on the energy consumption of buildings, Solar Energy, 70(3): 201–216.
- Santamouris, M. (2013). Using cool pavements as a mitigation strategy to fight urban heat island-A review. Solar Energy, 82(1): 301–312.
- Sarrat, C., Lemonsu, A., Masson, V. and Guedalia, D. (2006). Impact of urban heat island on regional atmospheric pollution. Atmospheric Environ., 40(10): 1743–1758.
- Skokovic, D., Sobrino, J. A., Jimenez-Munoz. J. C., Soria. G., Julien. Y., Mattar, C. and Jordi Cristobal (2014). Calibration and Validation of Land Surface Temperature for Landsat 8 – TIRS Sens. Land Product Validation and Evolution, ESA/ESRIN Frascati (Italy): 6-9, January 28-30,.
- Sobrino, J.A., Li, Z.L., Stoll, M.P. and Becker, F. (1996). Multichannel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data. Internat. J. Remote Sensing, 17: 2089–2114.
- Tan, J., Zheng,Y., Tang, X., Guo, C., Li, L. and Song, G. (2010). The urban heat island and its impact on heat waves and human health in Shanghai. Internat. J. Biometeorol., 54(1): 75–84.
- Xiaokang Kou, L.J., Yanchen, B., Shuang, Y. and Linna, C. (2016). Estimation of land surface temperature through blending MODIS and AMSR-E data with the Bayesian Maximum Entropy Method.Remote sensing, 108(5): 1-17, DOI: 10.3390/rs8020105.
- Zhao, S., Qin, Q., Yang, Y., Xiong, Y. and Qiu, G. (2009). Comparison of two Split-Window Methods for Retrieving Land Surface Temperature from MODIS Data. J. Earth Syst. Sci., 118(4): 345- 353.
- Ashley, E. and Lemay, L. (2008). Concrete’s contribution to sustainable development, from http://www.specifyconcrete.org/assets/docs/Concretes 20% Contribution to Sustainable Development.pdf.
- https://earthexplorer.usgs.gov