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2013 - 20192
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Author: Amit Kumar × End date: 2019 × Type: Thesis × Thesis Type: M.Tech. ×
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    ThesisItemOpen Access
    Comparison of MLP-ANN and W-ANN for SPI forecasting to assess meteorological drought
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-07) Amit Kumar; Singh, Pravin Vikram
    The accurate assessment of drought is an essential component for effective water resource management planning to mitigate adverse consequences of drought. The Standardized Precipitation Index (SPI) is a widely used index to characterize meteorological drought on a varying time scale. Information about Standardized Precipitation Index (SPI) at a place is vital for the assessment of drought. In this study, an approach to forecast Standardized Precipitation Index (SPI) has been attempted to assess meteorological drought in drought prone area of the country at different time scales. This approach involved application of Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN) and Wavelet Artificial Neural Network (W-ANN) to generate Standardized Precipitation Index values for different scales and denoted as, SPI-1, SPI-3, SPI-6, SPI-9, SPI-12 and SPI-24. To generate SPI values using these two models, the data set of Prabhani district in the state of Maharashtra was considered. The total data set of calculated values of SPI during 1971 to 2014 at various time scales was divided into three sets; (i) a training set, consisting of first 36 years data from January, 1971 to December, 2006; and (ii) a testing set, consisting of 4 years data from January, 2007 to December, 2010; and (ⅲ) a validation set, consisting of remaining 4 years data from January, 2011 to December 2014 for both the approaches. The SPI values at previous six-month lag were used to forecast current month SPI values and gamma test was used to decide the best combination of inputs for SPI forecasting. Both MLP-ANN and W-ANN models trained with the Levenberg Marquardt (LM) back propagation algorithm were developed using single hidden layer. The Root Mean Square Error (RMSE), Correlation Coefficient (r) and Coefficient of Efficiency (CE) statistical indices were adopted to evaluate the performance of these models. The SPI values generated by using best developed MLP-ANN and W-ANN models were compared with calculated values of SPI. The forecasted results indicate that for SPI-1, the performance of both MLP-ANN and WANN models was not satisfactory, however, MLP-ANN based model performed better than W-ANN model. For SPI-3, 6 and 9, the performance of W-ANN model was found to be better than MLP-ANN based model. In case of SPI-12 hand SPI-24, both the models were found to be performing satisfactorily, however, WANN model has a little bit edge over MLP-ANN. Interestingly, it was observed that the performance of both these models was found to be improving with increasing SPI time scale.
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    ThesisItemOpen Access
    Optimization of MIG welding parameters of dissimilar metals using Artificial Neural Network (ANN) and Genetic Algorithm (GA)
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2013-07) Amit Kumar; Jadoun
    Welding is a process by which we can join two similar or dissimilar metals very efficiently. With the help of welding, success of the weld can be achieved up to 100%. Welding is more useful because it is less costly as compared to other fabrication process like casting, forming, machining. In this study dissimilar metals of stainless steel of grade 304 and stainless steel of grade 316 are used in this study. We make 27 samples at different levels. Metal inert gas (MIG) welding is used in this study for welding because MIG welding is automatic machine and even less skill worker can operate it. Phoenix 301 MIG welding machine is used for welding. Joint strength is determined using the universal testing machine (UTM). In this study Artificial neural Network is a very important tool which relates input and output.. For classification Artificial Neural Network was built which shows some inter relationship between Input and Output parameter. ANN work similarly as biological neuron does work. GA is used to optimize selected MIG welding parameters input parameter (voltage, welding speed and current) and output parameter (tensile strength). Artificial neural network and Genetic Algorithm is used to design the experiment. The results were analysed using Artificial Neural Network (ANN) which is a part of MATLAB for the optimal parameters GA tool used which also a part of MATLAB. MIG welding is also known as metal inert gas welding and gas metal-arc welding (GMAW). Gas metal arc welding (GMAW) is widely used in industry due to its high metal deposition and ease of automation with better weld quality.