04042nas a2200481 a 4500 20260817163633.0 210126s2020 th uu m rtt 0| a1eng d .b12362219 AIT Diss. no.ICT-20-02 Sumanya Ngandee Assessment of rice crop yield prediction models based on big data analytics to support decision-making processes in the agricultural sector of Thailand Pathum Thani, Thailand : Asian Institute of Technology, 2020 95 leaves : ill. +1 online resource Dissertation ; no. ICT-20-02 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information and Communications Technologies, School of Engineering and Technology Thesis (Ph.D.) - Asian Institute of Technology, 2020 In this dissertation rice yield prediction models based on historical datasets (1989-2017) for the main type of in-season rice cultivated in Thailand was studied. Models were generated using the following Machine Learning (ML) algorithms: Generalized Linear Model (GLM), Feed-Forward Neural Network (FFNN), Support Vector Machine (SVM), and Random Forest (RF). The prediction models were evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R 2 statistic. The results show that the FFNN outperforms the other models for rice yield prediction accuracy. In addition, the results are shown to be better than the existing prediction techniques including Linear Regression (LR), the k-Nearest Neighbor algorithm (kNN), Naive Bayes (NB), J48 a decision tree, RF, SVM, and Artificial Neural Network (ANN). The results confirm that the FFNN, a.k.a., Multi-Layered Perceptron (MLP), which is a deep neural network, can simultaneously account for complex nonlinear relationships in high-dimensional datasets. While the Big-O complexity and the execution runtime for training of the FFNN exceed the other models, its execution of predictions takes the least execution runtime. The practical implication of this study is to improve the quality of agricultural information dissemination services to farmers, wholesalers, retailers, middlemen, processors, financial institutions, policy makers, and the general public for the development of Thailand{u2019}s agricultural sector, rice supply chains and the economy as a whole. Big data Analysis Decision Making Agriculture Thailand Crop yields Thailand Attaphongse Taparugssanagorn, Chairperson Chutiporn Anutariya, Examination committee Kuwornu, John K.M., Examination committee Ministry of Agriculture and Cooperatives (MOAC) Thailand , Scholarship donor Asian Institute of Technology Fellowship, Scholarship donor Asian Institute of Technology. Dissertations ; no. ICT-20-02 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B11249 .b12362219 mnait m 240418 0 210126 m a m 0 mnait mnait mnarc mnarc 40 67 20 Barcode : 30050120888309 CREATED : 2021-01-14 RECORD # : i13317416 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : - CREATED : 2021-01-14 RECORD # : i13317428 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050121029192 CREATED : 2022-09-21 RECORD # : i13401336 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050121029184 CREATED : 2022-09-21 RECORD # : i13401348 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 9315 9315 0 0 0 0 MAIN MAIN mnarc 2026-08-17 0 AIT Diss. no.ICT-20-02 30050120888309 2026-08-17 16:36:33 1 2026-08-17 40 0 0 0 0 MAIN MAIN mnarc 2026-08-17 0 AIT Diss. no.ICT-20-02 2026-08-17 16:36:33 2026-08-17 67 0 0 0 0 MAIN MAIN mnait 2026-08-17 50.00 0 AIT Diss. no.ICT-20-02 30050121029192 2026-08-17 16:36:33 1 2026-08-17 20 0 0 0 0 MAIN MAIN mnait 2026-08-17 50.00 0 AIT Diss. no.ICT-20-02 30050121029184 2026-08-17 16:36:33 2 2026-08-17 20