This paper explored the potential of Backpropagation Neural Network (BNN) and M5 model tree based regression approach to estimate the mean annual flood. Data used in this study were taken from an earlier study by Swamee et al. (J Water Resour Plan Manage 121:403-407, 1995) for 93 Indian catchments spread over the entire country. The relationship proposed by Swamee et al. (J Water Resour Plan Manage 121:403-407, 1995) was compared with the predictive accuracy of a BNN and M5 model tree approach. The data were analyzed using a tenfold crossvalidation. Comparison of the results showed that predictions with the backpropagation neural network fell within a scatter line of ±30% with a correlation coefficient of 0.975. Furthermore, predictions with the M5 model tree fell well within a scatter line of ±15% with a correlation coefficient as high as 0.994. The results also showed that predicted values with neural network and M5 model tree were within about 1.25 times the actual values. However, the predicted values obtained using the Swamee et al. (J Water Resour Plan Manage 121:403-407, 1995) approach fell much beyond the scatter line of ±50% and the predicted mean annual flood values were sometimes 2008 K.K. Singh et al.as high as eight times the actual values. The correlation coefficient with this approach was 0.897. The results from this study suggest that backpropagation neural network and M5 model tree-based modeling approaches are superior in accuracy to the model proposed by Swamee et al. (J Water Resour Plan Manage 121:403-407, 1995). This study also suggests that M5 model trees, being analogous to piecewise linear functions, have certain advantages over neural networks as they offer more insight into the generated model, are acceptable to decision makers and are very efficient in training, and always converge.
The present field experiment was conducted during the Rabi season of 2020-21 and 2021-22 at the Student's Instructional Farm of Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, Uttar Pradesh. The experiment comprised of 18 treatment combinations in Factorial randomized block deign with three replications. The result shown among the growth parameters the following assessed data: maximum plant height (48.56 cm) at 90 DAS, the number of nodules (31.27) at 60 DAS, dry weight of nodules (185.77 mg) at 60 DAS and the number of branches (18.12), similarly among yield attributes and yield viz. number of pod plant-1 (69.54), number of grains pod-1 (1.78), 100 grain weight (20.19 gm), grain yield (20.89 q ha-1) and stover yield (25.26 q ha-1) were recorded under T18 (P60+Zn2.5+ rhizobium) during the second year (2021-22) of experimentation. The application of phosphorous, zinc and rhizobium inoculation significantly increase growth, yield and yield attributes of chickpea during the both years of experiments. The present study shown that application of phosphorus, zinc and rhizobium inoculation along with recommended nitrogen and potassium could been an effective option for enhancing the chickpea growth parameters, yield attributes and yield of chickpea.
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