High-quality aerosol optical depth(AOD)data derived from MODIS is widely used in studying spatiotemporal trends of fine particulate matter(PM2.5)concentrations in eastern ***,the differences of MODIS-AOD(3/10 km dt;10...
详细信息
High-quality aerosol optical depth(AOD)data derived from MODIS is widely used in studying spatiotemporal trends of fine particulate matter(PM2.5)concentrations in eastern ***,the differences of MODIS-AOD(3/10 km dt;10 km DB)under four pollution situations(No-Po;Sl-Po;Mo-Po;Se-Po)are rarely *** this study,the MODIS-AOD and AODDifference spatial distributions from 2008 to 2017 are analyzed through annual/seasonal mean AOD maps generated at 0.1°×0.1°*** MODIS-AOD performances are validated using AERONET AOD data for various pollution situations and aerosol *** validations indicate that the 10-km DB algorithm provides the best performance,followed by 3-km dtand 10 km *** DB algorithm can provide spatially continuous AOD data for all seasons,whereas the dt algorithm often fails to yield valid data during *** validations under different pollution conditions illustrate that severe pollution significantly affects the validity of data obtained by the DB ***,the accuracy of dt-derived AOD data is robust against *** the same pollution conditions,the correlation coefficient of the DB algorithm is smaller than that of the dt *** quantity of valid data in the DB product is higher than those in dt products for all pollution conditions,especially under Se-Po.
Desertification is a grave threat to the environment and livelihoods. Desertification susceptibility assessment (DSA) plays a critical role in reasonable desertification prevention planning by mapping the extent, inte...
详细信息
Desertification is a grave threat to the environment and livelihoods. Desertification susceptibility assessment (DSA) plays a critical role in reasonable desertification prevention planning by mapping the extent, intensity, and classification of desertification. Numerous desertification maps have been produced using various DSA methods. However, the method of rapid desertification mapping by objectively discovering valuable DSA knowledge from experienced experts stored in such maps has rarely been explored. We propose a data-mining-based approach to mapping aeolian desertification that applies the decision tree (dt) C5.0 (C5) algorithm as a knowledge discovery tool to the reference map and corresponding environmental variables. The results of our case-study in Northern China show that the overall accuracy of aeolian desertification classification based on C5 is 86.69%, and the predicted map is highly consistent with the reference map. The dt algorithm outperforms the artificial neural network and naive Bayes approaches. Our results highlight the importance of selecting more representative training samples across where interleaved distributions of multiple aeolian desertification land exist when applying the dt algorithm. The findings of the present study are valuable for highlighting the significance of the data mining approach in DSA, with the growth of desertification maps. Given that aeolian desertification is a complex process coupling natural and human factors, and there are significant regional and scale differences in Northern China, further studies at a fine-scale regarding human factors deserve more attention.
In this study a new index is proposed for power system islanding prediction. The total energy absorbed by coherent synchronous generators during unplanned islanding conditions is formulated as an islanding predictor. ...
详细信息
In this study a new index is proposed for power system islanding prediction. The total energy absorbed by coherent synchronous generators during unplanned islanding conditions is formulated as an islanding predictor. Decision tree (dt) algorithm is utilised to extract the information gain of the proposed predictor over the input training samples. A comprehensive list of input scenarios, including island and non-island conditions is constructed. The rotor angles of synchronous generators are estimated by phasor measurements at generators' terminals and used for calculating the predictor over the input samples. The results of dt algorithm are rearranged as a set of if-then rules for practical applications. The proposed method is implemented in the 10-machine 39-bus New-England test system.
One of the major diseases found in women worldwide is Breast Cancer. More than 2.2M cases have been reported with about a 30% mortality rate in accordance with WHO. The rapid advancement of technology has led to break...
详细信息
Sandalwood is one of the most valuable woods in the world. However, today's counterfeits are widespread, it is difficult to distinguish authenticity. In this paper, similar genus (Dalbergia and Pterocarpus) and co...
详细信息
Sandalwood is one of the most valuable woods in the world. However, today's counterfeits are widespread, it is difficult to distinguish authenticity. In this paper, similar genus (Dalbergia and Pterocarpus) and confused species (Gluta sp.) of sandalwood were quickly and efficiently identified. Rapid identification model based on 1H NMR and decision tree (dt) algorithm was firstly developed for the identification of sandalwood, and the accuracy was improved by introducing the AdaBoost algorithm. The accuracy of the final model was above 95%. And the feature components between different species of sandalwood were further explored using UHPLC-QTOFMS and NMR spectrometry. The results showed that 183 compounds were identified, among which 99 were known components, 84 were unknown components. The H-1 NMR and C-13 NMR signals of 505 samples were assigned, among them, 14 compounds were attributed, characteristic chemical shift intervals with great differences in the model were analysed. Furthermore, the fragmentation pattern of different compounds from sandalwood, in both positive and negative ion ESI modes, was summarized. The results showed a potential and rapid tool based on dt, NMR spectroscopy and UHPLC-QTOFMS, which had performed great potential for rapid identification and feature analysis of sandalwood.
Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol...
详细信息
Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products are produced using the Deep Blue (DB) and Dark Target (dt) algorithms. Five aerosol types that are determined from Aerosol Robotic Network (AERONET) ground measurements are used to describe the global distribution of aerosol types in each algorithm. To assess the influence of LSR and the method used to determine aerosol type from aerosol retrievals, 10-km global aerosol products that cover 2013 are selected for validation using Level 2.0 aerosol observations from 175 AERONET sites. The variations in the retrieval accuracy of the DB and dt algorithms for different LSR values are analyzed by combining them with a global 10-km LSR database. Meanwhile, the adaptability of the MODIS products over areas covered with different aerosols is also explored. The results are as follows. (1) Compared with dt retrievals, the DB algorithm yields lower root mean squared error (RMSE) and mean absolut error (MAE) values, and a greater number of appropriate sample points fall within the expected error (EE). The DB algorithm shows higher overall reliability;(2) The aerosol retrieval accuracy of the DB and dt algorithms decline irregularly as the surface reflectance increases;the DB algorithm displays relatively high accuracy;(3) Both algorithms have a high retrieval accuracy over areas covered by weak absorbing aerosols, whereas dust aerosols and continental aerosols produce a low retrieval accuracy. The DB algorithm shows good retrieval results for most aerosols, but a lower accuracy for strong absorbing aerosols.
暂无评论