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Advisor: Murtaza Hasan
Other Titles: M Sc
Type: Thesis
Agrotags: cotton, planting, bacteria, exudates, transgenics, vegetative propagation, biological development, proteins, application methods, sowing
Abstract: Water is a one of the most vital and important resources on the planet earth. In the present situation of increasing human population, there is an increasing stress on available water resources. To meet the demand of growing population food production has to grow at the rate of growing population, which will lead to increase in agricultural water demand and therefore there is ever increasing need for irrigation water for better agricultural outputs. Therefore under these circumstances water resource management is of great importance. Regulation of operating policies of storage reservoir is vital component of water resource management. Therefore, there is a need to find out better operating policies of storage reservoirs mainly at the farm level in the form of farm ponds. The operating policies for on farm reservoir ensure judicious use of water at field level. Small reservoirs failed to serve the purpose of judicious water management due to faulty operating policies, which leads to poor distribution of water in space and time. In on farm reservoir the parameters like demand, supply and storage were highly uncertain and instantaneous. Therefore to develop operating rules of farm pond the soft computing techniques like fuzzy logic (FL) and Artificial Neural Network (ANN) can be employed effectively, which takes care of uncertainty and unreliability at the field level. So the present study was carried out to derive operational model for a farm pond of 3000 m3 capacity at Center for protected cultivation technology (CPCT), Indian Agricultural Research Institute, New Delhi, India. The farm pond was the important source of irrigation water of the farm of the area 10 ha. The Neuro-Fuzzy approach was used to develop the operational model and to derive operational rules for proper irrigation scheduling of the horticultural crops grown at CPCT. Based upon the inputs like crop water requirement, evaporation losses and farm pond inflow the Neuro-Fuzzy model predicting outflow of the reservoir was developed. Sugeno type Fuzzy inference system (FIS) was developed. The operating rules which were of the form “If-Then” were also developed using Adaptive NeuroFuzzy Inference System (ANFIS). Out of twenty seven rules only four best rules were selected. The developed operational model using Neuro-Fuzzy technique was validated statistically with new sets of input-output data set. The result of validation revealed that the developed Neuro-Fuzzy model was able to predict outflow with high accuracy. Coefficient of 59 determination (R2 ) between observed and predicted value was found to be 0.98. The model efficiency was also found to be very high i.e. 0.97. These results showed that the model has high accuracy and predictability. The linguistic form of operating rules of the developed soft computing Neuro-fuzzy model would be easy for the user to perceive the process. The operating policies of the farm pond would lead to sustainable water management at field level.
Description: T-8447
Issue Date: 2010
Appears in Collections:Theses

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