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Ayare, B. L.
- Multiplicative Seasonal ARIMA Modelling of Monthly Stream Flows of Choriti River
Abstract Views :203 |
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Authors
B. L. Ayare
1,
B. S. Dhekale
2
Affiliations
1 AICRP on Water Management, C.E.S. Wakawali, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, Ratnagiri (M.S.), IN
2 Bindan Chandra Krishi Vishwavidyalaya, Mohanpur (W.B.), IN
1 AICRP on Water Management, C.E.S. Wakawali, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, Ratnagiri (M.S.), IN
2 Bindan Chandra Krishi Vishwavidyalaya, Mohanpur (W.B.), IN
Source
International Journal of Agricultural Engineering, Vol 8, No 1 (2015), Pagination: 97-102Abstract
The multiplicative seasonal ARIMA (p,d,q) × (P,D,Q)s models of different orders were tried for modelling of monthly inflow of Choriti river of Konkan region of Maharashtra, based on 20 years data. The parameters of seasonal ARIMA models were estimated by fitting ARIMA models to differenced series (d=0 and D=1) at different lags. The goodness of fit of models was tested by Box-Pierce Portmanteau lack of fit test and comparison of historical and forecasted monthly inflows. The forecasted performance of the model was evaluated by using goodness of fit tests. Lower values of ischolar_main mean squared error; mean relative error and integral square error for multiplicative seasonal ARIMA (0,0,1) × (0,1,1)12 model indicated closer agreement between forested and historical monthly inflow series.Keywords
Autoregressive Integrated Moving Average Model, Forecasting, White Noise, Akaike Information Criteria.
- Performance Evaluation of Gravity Fed Inline Drip Irrigation System
Abstract Views :208 |
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Authors
B. L. Ayare
1,
R. T. Thokal
2
Affiliations
1 Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. B.S.K.K.V., Dapoli, Ratnagiri (M.S.), IN
2 AICRP on Irrigation Water Management, C.E.S. (Dr. B.S.K.K.V.), Wakawali (M.S.), IN
1 Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. B.S.K.K.V., Dapoli, Ratnagiri (M.S.), IN
2 AICRP on Irrigation Water Management, C.E.S. (Dr. B.S.K.K.V.), Wakawali (M.S.), IN
Source
International Journal of Agricultural Engineering, Vol 11, No 1 (2018), Pagination: 79-83Abstract
The field experiment was under taken on research farm of College of Agriculture Engineering and Technology, Dapoli, dist.- Ratnagiri (M.S.) to evaluation the performance of gravity fed inline drip irrigation system for cashew plantation. The system used inline drippers having the average discharge of 0.6 lit/hr. The design was done for 1 ha. area. The sizes (diameter) of the main line, sub main, lateral was computed as 40 mm, 12 mm and 6 mm, respectively, which was found to be less as compared to the conventional drip irrigation system. The emission uniformity of gravity fed inline drip irrigation system was found to be 85 per cent and was good for tested emitters as per ASAE interpretation. The vertical and radial movement of moisture was observed upto 60, 46 cm for 6 hrs and 80, 50 cm for 12 hrs, respectively which showed that the movement of moisture contour was faster in 6 hrs. as compared to 12 hrs. The moisture distribution pattern in gravel sandy loam lateritic soils showed that, the vertical movement of water was predominant than the lateral movement. The gravity fed inline drip irrigation system was found to be beneficial to small and marginal farmers of Konkan region of Maharashtra to provide the irrigation in remote areas.Keywords
Gravity Fed Drip Irrigation, Design, Moisture Distribution, Cost.References
- American Society of Agricultural Engineering (1985). Design installation and performance of trickle irrigation system; ASAE Engineering Practice, ASAE EP 405, ST. Joseph. MI.
- Brandt, A., Bresler, E., Diner, N., Heller, J. and Gold burg, D. (1971). Infiltration from a system source. I. Mathematical models. Soil Sci. Soc. Amer. Proc., 35 (50): 675-682.
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- Ravikumar, V., Santhana Bosu, S. and Kumar, V. (2001). Drip irrigation system design by energy gradient line approach for non-uniform outflow, Agric. Engg. J.Bangkok, Thailand,10 (3 & 4) : 181-190.
- Singh, R.K., Tiwari, K.N. and Kulkarni, J.J. (1977). Performance evaluation of micro-irrigation emitters. J. Institution of Engineers, 77: 47-51.
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- Performance of ANN for Rainfall-Runoff Prediction
Abstract Views :193 |
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Authors
Affiliations
1 Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli (M.S.), IN
1 Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli (M.S.), IN
Source
International Journal of Agricultural Engineering, Vol 12, No 1 (2019), Pagination: 112-117Abstract
The use of artificial neural network is becoming increasingly common in the analysis of hydrology and water resource problems. In present study, the observed rainfall and runoff data of four years (2010, 2011, 2013 and 2014) were used as input data. In ANN, input data was divided in three segment 70 per cent, 15 per cent and 15 per cent for training, validation and testing purpose, respectively. Rainfall-runoff relationship is an important component in water resource evaluation and therefore, the predicted runoff of 70 numbers of different types of model was tested statistically with observed runoff using statistical parameter, i.e. ischolar_main mean square error (RMSE), mean absolute error (MAE), co-efficient of determination (R2) and correlation (r). This study showed that out of 70 ANN architectures, ANN architectures 1-48-1 could be adopted to estimate runoff from ungauged watershed with rainfall as input.Keywords
ANN, Rainfall-Runoff Modeling, Co-Efficient of Determination, Correlation.References
- Anonymous (2000). ASCE task committee on application of the artificial neural networks in hydrology. Artificial Neural Network in Hydrology II; Preliminary Concepts. J. Hydrologic Engineering, 5 (2): 124-137.
- Junsawang, P., Asavanant, J. and Lursinsap, C. (2005). Artificial neural network model for rainfall-runoff relationship. Advanced Virtual and Intelligent Computing Center (AVIC). Department of Mathematics, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
- Mehendale, G. M. (2013). Study of rainfall runoff relationship using SCS-CN and ANN models for Bench Terraces, Dapoli. M. Tech Thesis, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, M.S. (India).
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- Salunke, J.R. (2013). Rainfall- runoff modelling of Priyadarshini watershed using artificial neural network. B. Tech. Thesis, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, M.S. (India).