An efficient model for the prediction of SMAP sea surface salinity using machine learning approaches in the Persian Gulf

Rajabi-Kiasari, Saeed; Hasanlou, Mahdi

doi:10.1080/01431161.2019.1701212

Cited by 23 publications

(13 citation statements)

References 61 publications

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“…Generally, ensemble approaches are algorithm-independent, and impose no special restrictions on selection of the benchmark models. Based on the results of previous studies, we selected three stable machine learning models, XGBoost, RandomForest, and LightGBM as our basic model, all of which have been shown to have ability to estimate the OSTS [26,46,47,60].…”

Section: Methodsmentioning

confidence: 99%

An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea

Liu

Chi

et al. 2022

Remote Sensing

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Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface data and Argo data in the South China Sea (SCS). The input data include sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), and geographic information (including longitude and latitude). We select three stable machine learning models, namely, extreme gradient boosting (XGBoost), RandomForest and light gradient boosting machine (LightGBM) as our benchmark models, and then use an artificial neural network (ANN) technique to combine outputs from the three individual models. The proposed Ens-ML model using sea surface data only by SSH, SST, SSS, and SSW performs less satisfactorily than that considering the contribution of geographical information, indicating that the geographical information is essential to estimate the OSTS accurately. The estimated OSTS from the Ens-ML model are compared with Argo data. The results show that the proposed Ens-ML model can accurately estimate the OSTS (upper 1000 m) in the SCS, which is relatively more accurate and precise than the individual models. The performance of the Ens-ML model also varies with season, and better estimation is obtained in winter, which is probably due to stronger mixing and weaker stratification. This study shows the great potential and advantage of the multi-model ensemble of machine learning algorithm for the ocean’s interior information retrieving, showing great potential in expanding the scope of ocean observations.

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Section: Methodsmentioning

confidence: 99%

An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea

Liu

Chi

et al. 2022

Remote Sensing

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“…While modeling the forecasting method, both the features which exhibit strong, multiple collinearities and the features which have a low correlation with the dependent variable should be eliminated for enhancing the prediction accuracy. Thus, we choose the Pearson correlation analysis to test the correlation of all features and the dependent variable [33,34]. The calculation of Pearson correlation analysis is as Equation (1).…”

Section: Feature Selectionmentioning

confidence: 99%

Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method

et al. 2020

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The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online taxi-hailing demand. First, we analyze the relation between taxi demand and online taxi-hailing demand. Next, we propose six models containing different information based on backpropagation neural network (BPNN) and extreme gradient boosting (XGB) to forecast online taxi-hailing demand. Finally, we present a real-time online taxi-hailing demand forecasting model considering the projected taxi demand (“PTX”). The results indicate that including more information leads to better prediction performance, and the results show that including the information of projected taxi demand leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R2 from 0.845 to 0.853. The analysis indicates the demand regularity of online taxi-hailing and taxi, and the experiment realizes real-time prediction of online taxi-hailing by considering the projected taxi demand. The proposed method can help to schedule online taxi-hailing resources in advance.

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“…These algorithms employ techniques such as random forests and support vector regressions to estimate SSS [30]. The machine-learning algorithms are capable to work with multifaceted data with a large number of predictor variables, and have gained popularity in a wide range of classification and estimation problems (including SSS retrievals from satellite data) [31], [32]. Recent studies reported a low accuracy in SSS retrievals in coastal oceans, estuaries, and outlets of major river systems [33][34].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

2022

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Despite the recent advances in satellite-based L-band microwave radiometry and retrieval algorithms to provide a unique capability to measure sea surface salinity (SSS) from space and explore its utility for understanding mesoscale dynamics, global ocean circulation, vertical mixing, evaporation rates and climate oscillations, SSS retrieval from L-band microwave radiometric data from the NASA-SMAP (Soil Moisture Active Passive) mission is often biased with systematic errors on larger temporal, spatial scales and random errors on short-time and length scales. To improve the SSS retrieval from SMAP data, we developed a robust algorithm based on a machine-learning approach with high-resolution in-situ data from the Ocean Moored Buoy Network in the Northern Indian Ocean (OMNI), which includes the Arabian Sea (AS) and Bay of Bengal (BoB)). The new algorithm was rigorously trained, tested and validated using the in-situ SSS time series measurements from the AS and BoB. Several sensitive variables were examined and used to improve the SSS estimatessuch as radiometric and ancillary data from the satellite observations, sea-surface wind and precipitation from the ERA5 data, and SSS/ SST from the OMNI buoy measurements. The OMNI timeseries measurements provided the spatially averaged satellite products to characterize the variability and gross features of SSS in BoB and AS waters on weekly, monthly, seasonal and annual time scales. The systematic validation of SMAP SSS products on a range of spatio-temporal scales showed that the new algorithm improved the SSS estimates by more than 15% in open ocean waters and 25% in river-discharge and precipitation-dominated regions in BoB and AS, when compared to the standard (operational) algorithm. Further analysis demonstrated that the new algorithm reduced clear biases and significant anomalies in SMAP SSS retrievals in the regions of river runoff, surface-freshened ocean and intense tropical cyclones, and captured synoptic/mesoscale SSS features and their seasonal variations in the North Indian Ocean.

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An efficient model for the prediction of SMAP sea surface salinity using machine learning approaches in the Persian Gulf

Cited by 23 publications

References 61 publications

An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea

An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea

Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

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