International Journal of Science, Engineering and Technology
Proceeding of the Conference
Comparison of Phenological Weather indices based Statistical and Machine Learning Models for Soybean Yield Forecasting in Pantnagar, Uttarakhand
Authors- Yunish Khan, Vinod Kumar
Abstract- – Early information exchange regarding predicted crop production could play a role in lowering the danger of food insecurity. Predicting crop yields is one of the more difficult tasks in the farming industry. Several investigations have been conducted in the agricultural field to predict increased crop production using the machine learning algorithm Artificial Neural Network (ANN) and statistical model Stepwise Multiple Linear Regression (SMLR). In this study eight multivariate weather-based models including stepwise multiple linear regression (SMLR), principal component analysis (PCA), artificial neural network (ANN) and combinations of them using weather indices and direct weather variables were investigated by fixing 80% of the data for calibration and the remaining dataset for validation to predict soybean yield for Pantnagar, Uttarakhand. Based on the value of R2 (0.95) and nRMSE (7.16%) during calibration stage, the PCA-ANN-W model performed excellent, becoming the best model for soybean prediction compared to other models in the study region. The overall ranking based on the performances of the models can be given as: PCA-ANN-W > ANN-WI > SMLR-W > SMLR-WI ≈ PCA-SMLR-WI > ANN-W > PCA-ANN-WI > PCA-SMLR-W. The study results indicated that PCA-ANN-W and ANN-WI model performed well for the study region as compared to other models.
DOI: 10.61463/ijset.icmmsa-2023.101
Estimation of Reliability in a Consecutive linear/circular k-out-of-n system based on Weighted Exponential-Lindley distribution
Authors- Sunita Sharma; Vinod Kumar
Abstract- -Consecutive k-out-of-n systems have gained significant attention and found diverse applications across various domains. This research article introduces a Classical and Bayesian approach for reliability estimation in Consecutive linear/circular k-out-of-n: F systems using the Weighted Exponential-Lindley distribution. By employing this distribution to model component lifetimes, we obtained maximum likelihood and Bayesian estimates for reliability using squared error loss function. In cases where exact forms are unattainable, Lindley’s approximation and the Markov chain Monte Carlo method are utilized to derive Bayes estimates. We also examined mean time to failure and constructed credible intervals to estimate Bayes reliability. To assess and compare the effectiveness of these estimators, we carried out a Monte Carlo simulation study.