scholarly journals Comparative analysis on the fuel consumption prediction model for bulk carriers from ship launching to current states based on sea trial data and machine learning technique

2021 ◽  
Author(s):  
Tran Tien-Anh
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Prediction Model ◽  
Fuel Consumption ◽  
Trial Data ◽  
Machine Learning Technique ◽  
Sea Trial ◽  
Bulk Carriers ◽  
Learning Technique ◽  
Consumption Prediction

Developing a new rigorous drilling rate prediction model using a machine learning technique

2020 ◽  
Vol 192 ◽  
pp. 107338
Author(s):  
Mohammad Mehrad ◽  
Mahdi Bajolvand ◽  
Ahmad Ramezanzadeh ◽  
Jalil Ghavidel Neycharan
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Drilling Rate ◽  
Machine Learning Technique ◽  
Rate Prediction ◽  
Learning Technique

Multi-task learning framework for predicting water quality using non-linear machine learning technique

2021 ◽  
pp. 1-13
Author(s):  
D. Senthilkumar ◽  
D. George Washington ◽  
A.K. Reshmy ◽  
M. Noornisha
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Machine Learning Technique ◽  
Water Quality Prediction ◽  
Task Learning ◽  
Learning Technique ◽  
Non Linear ◽  
Linear Machine

Predicting the quality of water is a very important issue in an ecosystem and it can be used to control the increase of water contamination. Also, water quality prediction is a prominent complex non-linear multi-target learning problem and extracting a relevant subset of features from a large number of features with multiple targets is a challenging task. Existing water quality prediction model not focused on multi-target learning process simultaneously and not identifying the non-linear relationship between the features and target variables. Therefore, this study proposes a multi-task learning method dealing with multi-target regression using non-linear machine learning technique. Finally, experiments are conducted to build a prediction model based on the proposed methods to evaluate accuracy on water quality dataset. The experimental results indicate that our method increases the overall accuracy of the experimental dataset compared with the existing methods with the reduced number of significant features.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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