Chlorophyll‐a (Chl‐a) is one of the most important indicators of the trophic status of inland waters, and its continued monitoring is essential. Recently, the operated Sentinel‐2 MSI satellite offers high spatial resolution images for remote water quality monitoring. In this study, we tested the performance of the three well‐known machine learning (ML) (random forest [RF], support vector machine [SVM], and Gaussian process [GP]) and the two novel ML (extreme gradient boost (XGB) and CatBoost [CB]) models for estimation a wide range of Chl‐a concentration (10.1–798.7 μg/L) using the Sentinel‐2 MSI data and in situ water quality measurement in the Tri An Reservoir (TAR), Vietnam. GP indicated the most reliable model for predicting Chl‐a from water quality parameters (R2 = 0.85, root‐mean‐square error [RMSE] = 56.65 μg/L, Akaike's information criterion [AIC] = 575.10, and Bayesian information criterion [BIC] = 595.24). Regarding input model as water surface reflectance, CB was the superior model for Chl‐a retrieval (R2 = 0.84, RMSE = 46.28 μg/L, AIC = 229.18, and BIC = 238.50). Our results indicated that GP and CB are the two best models for the prediction of Chl‐a in TAR. Overall, the Sentinel‐2 MSI coupled with ML algorithms is a reliable, inexpensive, and accurate instrument for monitoring Chl‐a in inland waters.
Practitioner points
Machine learning algorithms were used for both remote sensing data and in situ water quality measurements.
The performance of five well‐known machine learning models was tested
Gaussian process was the most reliable model for predicting Chl‐a from water quality parameters
CatBoost was the best model for Chl‐a retrieval from water surface reflectance
Based on guides RG 1.109, RG 1.111 published by United States Nuclear Regulatory Commission (USNRC) our research concentratesinassessing radiation doses caused by radioactive substances released from the nuclear power plant (NPP) Ninh Thuan 1 under the scenario of normal operation using software package NRCDose72 provided by the USNRC. The database including the released radioactive nuclides, meteorology, terrain, population and agricultural production activities have beencollectedand processed to build the input data for the model calculation. The wind rose distribution obtained from the meteorological data in a five-year period from 2009-2013 showed that the radioactive nuclides released to environment spread in two main wind directions which are North East and South West. The X/Q (s/m3) and D/Q (s/m2) qualities which are, respectively, the ratio of activity concentration to release rateand that of deposition density of radioactive nuclides to release rate were calculated within an area of 80 km radius from the NPP site using XOQDOQ. Population doses were calculated using GASPAR. The XOQDOQ and GASPAR are two specific softwares in NRCDose72 package.
According to the United Nations Scientific Council on the Effects of Atomic Radiation(UNSCEAR), the global average dose level for the community is 2.4mSv/year. People living in the areas with high levels of radiation will cause adverse effects on their health. There are two main components that cause the dose of radiation, mainly due to the inhalation of radon and the extra dose of gamma radiation. The paper presents the results of assessment of natural effective radiation doses on the basis of the projected outpatient dosimetry in 70 households living in Mau and Mo village of Nam Xe, Phong Tho distrist, Lai Chau province.
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