In the past few years, the location prediction played a critical role in many applications like intelligent self-learning vehicle, ocean location prediction because of the security and speed issues of GPSs. In this study, we proposed a model for location prediction on Oman’s gulf using a NetCDF Data set. The proposed model is based on classification and regression which means it first mapped the data in a region on Oman’s Gulf using classification and then using regression models to predict a specific location. This progress effect both response time and error of the system. And to the best of our knowledge, no researches are using the same idea. We used multiple classification models for classification tasks (both ensemble models and simple models) and two regression models (linear and XGboost regressor). The result shows reduce of man square error after using classification for regression task. Also, the result and explanation of the data capturing model are provided in the paper.
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Ghorbani, A., & Khalilabadi, M. R. (2020). Positioning Using Classification and Regression: Case study of Oman Sea. International Journal Of Coastal, Offshore And Environmental Engineering, 5(3), 35-41. doi: 10.22034/ijcoe.2020.149351
Ali Ghorbani; Mohammad Reza Khalilabadi. "Positioning Using Classification and Regression: Case study of Oman Sea". International Journal Of Coastal, Offshore And Environmental Engineering, 5, 3, 2020, 35-41. doi: 10.22034/ijcoe.2020.149351
Ghorbani, A., Khalilabadi, M. R. (2020). 'Positioning Using Classification and Regression: Case study of Oman Sea', International Journal Of Coastal, Offshore And Environmental Engineering, 5(3), pp. 35-41. doi: 10.22034/ijcoe.2020.149351
Ghorbani, A., Khalilabadi, M. R. Positioning Using Classification and Regression: Case study of Oman Sea. International Journal Of Coastal, Offshore And Environmental Engineering, 2020; 5(3): 35-41. doi: 10.22034/ijcoe.2020.149351