Animal identification is a process to identify and track animals. It is done for a variety of reasons including verification of ownership, biosecurity control, record keeping, efficient farm management, registration, insurance and presentation of theft of animals. Identification of animal in livestock enterprise is of immense importance to draw attention regarding their status in production as well as performance. Thus, proper identification of animal is very important for understanding the need of record keeping and will provide a base to improve the management of herd. The use of RFID for automation will also aid to minimize labour input, thus allowing each farmer to cater for more cows, or enabling farmers to have more time to spend on other activitieseither way, maximizing results from their input.
The conceptual and physical mathematical model of rainfall-runoff modeling uses various parameters such as land use land cover, soil type classification, rainfall, atmospheric data such as temperature, evapotranspiration, solar radiation and wind speed, etc. But these data may not be available for developing countries and data scares semi-arid watershed. Also, the problem is even more critical for ungauged catchments and where manual record is maintained of water level and rainfall data. To address this issue, trend analysis is performed using Mann-Kendall test and Sen’s slope test which shows significant trend change stressing the need for new method for runoff prediction for better water resource management. In this study, a total of four models namely nonlinear autoregressive model with exogenous inputs lumped (LNARX), nonlinear autoregressive model with exogenous geomorphometrically processed inputs (GNARX), wavelet nonlinear autoregressive model with exogenous inputs (WLNARX) and nonlinear autoregressive model with exogenous geomorphometrically processed inputs (WGNARX). Ten models with different input combinations were selected based on their performance are analyzed for all the four networks. The best performing model for these networks is model no. 6 with WGNARX network with NSE 0.97 and RMSE 0.97 and with least value of RMSE. This method can be applied to data scarce region where data available are available for shorter duration and helpful for ungauged catchments also.
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