Improving Environmental Prediction by Assimilating Auxiliary Information

Yang, Yong; Zhang, C. T.; Zhang, R. X.; Christakos, George

doi:10.3808/jei.201500316

Cited by 7 publications

(7 citation statements)

References 35 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, BME is capable of considering uncertainties contained in the data. The method has been successfully applied to numerous areas, such as air pollution, soil properties, water demand and disease [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. It has also achieved good results in the gap-filling of remote sensing data [62][63][64].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method

Zhang

Chen

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

The land surface temperature (LST) is a key parameter used to characterize the interaction between land and the atmosphere. Therefore, obtaining highly accurate, spatially consistent and temporally continuous LSTs in large areas is the basis of many studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product is commonly used to achieve this. However, it has many missing values caused by clouds and other factors. The current gap-filling methods need to be improved when applied to large areas. In this study, we used the Bayesian maximum entropy (BME) method, which considers spatial and temporal correlation, and takes multiple regression results of the Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), longitude and latitude as soft data to reconstruct space-complete daily clear-sky LSTs with a 1 km resolution for China’s landmass in 2015. The average Root Mean Square Error (RMSE) of this method was 1.6 K for the daytime and 1.2 K for the nighttime when we simultaneously covered more than 10,000 verification points, including blocks that were continuous in space, and the average RMSE of a single discrete verification point for 365 days was 0.4 K for the daytime and 0.3 K for the nighttime when we covered four discrete points. Urban and snow land cover types have a higher accuracy than forests and grasslands, and the accuracy is higher in winter than in summer. The high accuracy and great ability of this method to capture extreme values in urban areas can help improve urban heat island research. This method can also be extended to other study areas, other time periods, and the estimation of other geographical attribute values. How to effectively convert clear-sky LST into real LST requires further research.

show abstract

Section: Introductionmentioning

confidence: 99%

Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method

Zhang

Chen

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…To accurately quantify the abnormal level of condition parameter is another key point for detecting incipient WT fault. Recently, the information entropy has proved its suitability for condition monitoring, fault detection and risk assessment (Cabal et al, 2010;Zhang et al, 2010;Li et al, 2012;Ai et al, 2013;Yang et al, 2015;Nourani et al, 2015). For instance, faulted rotor bars were detected through the analysis of vibration signals based on information entropy (Cabal et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

A Generalized Model for Wind Turbine Faulty Condition Detection Using Combination Prediction Approach and Information Entropy

Chen¹,

Chen²,

Li³

et al. 2018

J ENV INFORM

View full text Add to dashboard Cite

A generalized model for detecting the incipient wind turbine (WT) faulty condition based on the data collected from wind farm supervisory control and data acquisition (SCADA) system is proposed in this paper. The linear combination prediction approach and the information entropy are integrated to develop the generalized model, in which the linear combination prediction approach improves the accuracy and generalization performance of the model, and the information entropy of prediction residual quantifies the abnormal level of the condition parameter. SCADA datasets were selected to establish the prediction models of WT condition parameters that are dependent on environmental conditions such as ambient temperature and wind speed. The combination prediction models of WT condition parameters were developed based on different data mining algorithms such as Back propagation neural network (BPNN) algorithm, radial basis function neural network (RBFNN) algorithm and least square support vector machine (LSSVM) algorithm. The information entropy was utilized to extract useful information from residuals of the prediction models for WT faulty condition detection. Finally, the proposed method has been used for real 1.5 MW WTs with doubly fed induction generators (DFIG). Through investigation of cases of actual WT faults, the effectiveness of the proposed WT imminent fault identification approach was verified.

show abstract

“…In addition, a simpler model structure means that the propagation of uncertainty from different sources is easier to assess. The use of data-driven models, such as neural networks, statistical methods or regression-based techniques (e.g., Li et al, 2015b, Li et al, 2015cYang et al, 2015), has been widespread in hydrology, particularly for short term daily flow rate forecasts, using a variety of input variables (Garen, 1992;Zealand et al, 1999;Campolo et al, 1999;Schilling and Walter, 2005;Adamowski and Sun, 2010;Duncan et al, 2011;Li et al, 2015a;Nourani et al, 2015). A recent regression based study predicted flow in the Bow River in Calgary, using a base difference regression model (Veiga et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression

Khan¹,

Valeo²

2016

J ENVIRON INFORM

View full text Add to dashboard Cite

ABSTRACT.A fuzzy linear regression (FLR) method is proposed that uses real-time data to accurately predict daily peak flow rate for the Bow and Elbow Rivers in southern Alberta. FLR model performance was compared to a non-fuzzy, error-in-variables model (EIV). Mean daily flow rate, with a delay of one, two, three or seven days was used as the independent variable. In implementing the FLR, a unique hybrid modelling approach was devised that treated peak flow rate as probabilistic and mean daily flow rate as possibilistic. Three gauge errors, 5%, 10% and 20%, were tested and compared to quantify uncertainty in observed flow rate. The research proposed a new method of computing the exceedance probability of peak flow rate using fuzzy numbers. NSE, PBIAS and RSR and a proposed rating system were used to evaluate and compare the methods. Two different calibration schemes were used, including a quasi-real time system. The tests demonstrated that FLR with a one day lag was a very good predictor of peak flow rate and outperformed EIV for two stations on the Bow River. A test dataset from the floods of June 2013 in Calgary was used for risk assessment. The FLR results demonstrated higher flexibility and sensitivity to the flood as it approached Calgary. The fuzzy method was able to capture the peak flow rate for the majority of the high flow rate days, while the EIV model was unable to predict this data within the 95% confidence interval.

show abstract

Improving Environmental Prediction by Assimilating Auxiliary Information

Cited by 7 publications

References 35 publications

Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method

Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method

A Generalized Model for Wind Turbine Faulty Condition Detection Using Combination Prediction Approach and Information Entropy

Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression

Contact Info

Product

Resources

About