Refine your search
Collections
Co-Authors
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
Singh, Bhaskar Pratap
- Weather Parameter Based Crop Planning in Tarai Region of Uttarakhand
Abstract Views :184 |
PDF Views:0
Authors
Affiliations
1 Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
3 Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu Univesity, Varanasi (U.P.), IN
1 Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
3 Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu Univesity, Varanasi (U.P.), IN
Source
International Journal of Agricultural Engineering, Vol 10, No 2 (2017), Pagination: 360-366Abstract
The major weather parameters like temperature, relative humidity, rainfall, wind speed and sunshine hour for a period of 43 years were collected and analyzed. This was done for crop planning and to develop an appropriate irrigation scheduling for different crops. The annual rainfall record indicated that in 40.47 per cent cases the normal rainfall (average ± 19%) was received in the study area, whereas, the per cent of below normal and above normal rainfall was found as 33.33 and 26.20 per cent, respectively. The highest PET was obtained in April and the lowest in December. The maximum net irrigation requirements for Rabi and Kharif season crops were found in February, March, April, June, September, October and November months. June to September months received the highest rainfall when the rainfall was received about 86 per cent of the total amount of annual rainfall. It appears that surplus rainfall (Rainfall>PET) during mid-June to August received and it can be harvested and use in high irrigation demand months.Keywords
Rainfall, Probability Analysis, Irrigation Water Requirement, Crop Planning.
References
- Agnihotri, Y., Madukar, R.M. and Singh, P. (1986). Weekly rainfall analysis and agricultural droughts at Chandigarh. Vayumandal, 16 : 54-56.
- Allen, R.G. (1996). Assessing integrity of weather data for reference evapotranspiration estimation. J. Irrig. Drain. Engg., 122 (2) : 97–106.
- Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. (1998). Crop evapotranspiration guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper no. 56, Rome, Italy.
- Chiew, F.H.S., Kamaladasa, N.N., Malano, H.M. and McMahon, T.A. (1995). Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agric Water Manage., 28 : 9–21.
- Doorenbos, J. and Pruitt, W.O. (1977). Irrigation guidelines for computing crop water requirements. Irrigation and Drainage paper No. 24, FAO, Rome, Italy.
- Gupta, R.K., Rambabu and Tejwani, K.G. (1975). Weekly rainfall of India for crop planning programme. Soil Cons. Digest, 3 : 31-39.
- Liang, L., L. Li. and Liu, Q. (2010). Temporal variation of reference evapotranspiration during 1961-2005 in the Taoer river basin of Northeast China. Agril. For. Meteorol., 150 : 298-306.
- Liou, Y.A. and Kar, S.K. (2014). Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms-A Review.Energies, 7: 2821-2849; doi:10.3390/en7052821.
- Mulat, D., Guta, F. and Ferede T. (2004). Agricultural development in Ethiopia: are there alternatives to food aid? Research report, Addis Ababa.
- Ray, C.R., Senapati, P.C. and Lal, R. (1980). Rainfall analysis for crop planning, Gopalpur (Orissa). J. Agric. Engg., 17: 1-8.
- Sharma, H.C., Chauhan, H.S. and Sewa Ram (1979). Probability analysis of rainfall for crop planning. J. Agric. Engg.,16 : 887-896.
- A Comparative Study of Artificial Intelligence and Conventional Techniques for Rainfall-Runoff Modeling
Abstract Views :312 |
PDF Views:0
Authors
Affiliations
1 Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu Univesity, Varanasi (U.P.), IN
3 Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
1 Department of Soil and Water Conservation Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand), IN
2 Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu Univesity, Varanasi (U.P.), IN
3 Department of Irrigation and Drainage Engineering, 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: 441-449Abstract
The essential for accurate modeling of the rainfall–runoff process has grown rapidly in the past decades. However, considering the high stochastic property of the process, many models are still being developed in order to define such a complex phenomenon. In this study, two AI-based models which are reliable in capturing the periodicity features of the process are introduced for river rainfall–runoff modeling. In the first model, the ANN model, an ANN is used to five different type training algorithms namely momentum, Quickprop, Delta-Bar-Delta, Conjugate Gradient and Levenberg Marquardt. In the second model, ANFIS model trained used to two different type membership function (MFs) viz., Gaussian and generalized bell and conventional techniques was used multiple linear regression (MLR). The artificial intelligence performed better than the conventional techniques for rainfall-runoff modelling of study area. The ANFIS models performing the best results, ANN models gives the satisfactory results and MLR model having poor result in runoff prediction for Arpa River basin. Also gamma test (GT) was used for identifying the best input combination of input variables.Keywords
Artificial Neural Network, Adaptive Neural-Fuzzy Inference System, Multiple Linear Regression, Gamma Test.References
- Daniell, T.M. (1991). Neural networks applications in hydrology and water resources engineering. International Conference on Hydrology and Water Resources Symposium, 3(3): 797-802.
- Dawson, C.W. and Wilby, R.L. (1998). An artificial neural network approach to rainfall-runoff modeling. Hydrological Sci. J., 43(1): 47-66.
- Fernando, D.A.K. and Jayawardena, A.W. (1998). Runoff forecasting using RBF networks with OLS algorithm. J. Hydrol. Engg., 3(3): 203-209.
- Folorunsho, J. Garba, S. Obiniyi, A.A. and Ajibade, A.O. (2014) A comparison of ANFIS and ANN–based models in River discharge forecasting. New Ground Res. J. Physical Sci., 1 (1): 1-16.
- He, K., Zhang, X., Ren, S. and Sun, J. (2014). Spatial pyramid pooling in deep convolutional networks for visual recognition. arXiv:1406:4729.
- Helsel, D.R. and Hirsch, R.M. (2002). Statistical Methods in Water Resources. Techniques of Water-Resources Investigations of the U. S Geological Survey Book 4, Hydrologic Analysis and Interpretation, Chapter A3.
- Jain, S.K. and Srivastva, D.K. (1999). Application of ANN for reservoir in flow prediction and operation. J. Water Resour. Planning & Manage, 125(5): 263-271.
- Jiang, S., Ren, L., Hong, Y., Yong, B., Yang, X., Yuan, F. and Ma, M. (2012). Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method. J. Hydrol., 452–453(0): 213-225.
- Khan, M.S. and Coulbaly, P. (2006). Bayesian neural network for rainfall-runoff modeling. Water Resour. Res., 42(7): 225-234.
- Kisi, O., Shiri, J. and Tombul, M. (2013). Modeling rainfall-runoff process using soft computing techniques.Computers & Geosciences, 51 : 108–117.
- Kisi, O. (2015). Stream flow forecasting and estimation using least square support vector regression and adaptive Neuro-Fuzzy embedded Fuzzy c-means clustering. Water Resourc. Mgmt., 29(14): 5109-5127.
- Lin, G.F. and Wu, M.C. (2011). An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model. J. Hydrol., 405(3-4): 439-450.
- Lorrai, M. and Sechi, G.M. (1995). Neural nets for modeling rainfall-runoff transformations.Water Resour. Manage, 9 : 299-313.
- McCulloch, W. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bull. Mathematical Biophysics, 5:115-133.
- Moradkhani, H., Hsu, K.L., Gupta, H.V. and Sorooshian, S. (2004). Improved stream flow forecasting using self-organizing radial basis function artificial neural networks. J. Hydrol., 295(1–4): 246-262.
- Partal, T. (2009). River flow forecasting using different artificial neural network algorithms and wavelet transform.NRC Res. Press, 36: 26-39.
- Rajurkar, M.P., Kothyari, C. and Chaube, U. (2004)Modeling of Daily Rainfall-Runoff Relationship with Artificial Neural Network. J. Hydrol., 285 : 96-113.
- Shafaei, M. and Kisi, O. (2016). Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models. Neural Comput. & Appl., 1 : 1-14.
- Shamseldin, A.Y., O’connor, K.M. and Nasr, A.E. (2010). A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall-runoff models. Hydrol. Sci. J., 52(5): 896-916.
- Shrivastav, M.B., Gandhi, H.M., Ramanuj, P.K., Chudasama, M.K. and Joshi, J.A. (2014). Prediction of runoff using artificial neural networks (A Case study of Khodiyar Catchment Area). Internat. J. Scientific Res. & Dev., 2 ISSN-2321-0613.
- Srinivasulu, S. and Jain, A. (2006). A comparative analysis of training methods for artificial neural network rainfall–runoff models. Appl. Soft Computing, 6(3): 295-306.
- Toth, E. (2009). Classification of hydro-meteorological conditions and multiple artificial neural networks for stream flow forecasting.Hydrolog.Earth Sys. Sci., 13 : 1555-1566.