Evaluation of Lake Eutrophication Based on the HJ-1 Satellite Multispectral Data

2014 ◽  
Vol 519-520 ◽  
pp. 1184-1187
Author(s):  
Shun Yao Jiang ◽  
Qin Xue Xiong ◽  
Jian Qiang Zhu
Keyword(s):  
Monitoring And Evaluation ◽  
Quality Data ◽  
Radiometric Calibration ◽  
Water Quality Data ◽  
Multispectral Data ◽  
Independent Variables ◽  
The Mean ◽  
Study Case ◽  
Mean Time ◽  
Trophic Level Index

Chinese domestic satellite HJ-1 was launched in 2008, now it has become a significant resource for environment monitoring because its Multispectral data are characterized by 2-day temporal resolution, 30 m spatial resolution and 700 km breadth. A study case was made that HJ-1 multispectral data were used for retrieving aggregative trophic level index (TLI()) of water body of lakes in Wuhan, China. The aim of this study is to evaluate the probability of HJ-1 Multispectral data on estimating the eutrophic level of inland water. At first, the TLI() of sampling spots of some lakes in Wuhan were calculated using the monitoring water quality data. In the mean time, the NDVI of corresponding spots were calculated from the HJ-1 multispectral data which had been taken radiometric calibration and geometric correction beforehand. After that, a remote sensing inversion model for TLI() had been built through linear regression using the NDVI as independent variables. Finally, the TLI() of all water bodies of Wuhan lakes were inversed with this model and a map for its spatial distribution was drawn up. Results demonstrated that there were good linear correlation relationships between the TLI() and NDVI of HJ-1 Multispectral data, and the distribution of inversed TLI() of the lakes accorded with the reality quite well. According to the above, an inference can be made that the methods to evaluate lakes eutrofication based on the HJ-1 satellite multispectral data may provide a cheap and rapid way for real-time monitoring and evaluation of lakes eutrophication.

scholarly journals Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

Study on Social Risk Appraise of Construction Project in China — A Case from Guangzhou

2012 ◽  
Vol 174-177 ◽  
pp. 2916-2924 ◽  
Author(s):  
Lin Chen ◽  
Kai Ze Wu ◽  
Jian Hui Tan ◽  
Ming Fei Li
Keyword(s):  
Construction Project ◽  
Social Risk ◽  
Reference Groups ◽  
Depth Interview ◽  
The Social ◽  
The Mean ◽  
Study Case ◽  
Spss Software ◽  
Mean Time ◽  
Proper Solutions

Aimed to the social risk appraise of construction project, the qualitative and quantitative analysis, as well as the theory and empirical research methodologies are used in this paper, and taking a Demolition and Relocation Project of Guangzhou as the Study Case. Based on questionnaire investigation and depth interview to the samples of reference groups in this project, the potential social risk and the real appeals of the reference groups are described systematically and objectively in this paper. In the mean time, the SPSS software is used to analyses the statistical data. Finally, focused on above analysis, the relevant suggestions to prevent the social risk are proposed. Appraising conclusion shows that most sensitive people, within the scope of demolition, agree on the quasi decision-making matters on the construction project, and at least, they are not opposed to it. However, there are several practical issues to which we need to pay close attentions and to require proper solutions. Before these issues are solved, suspending the demolition is suggested.

scholarly journals Application of multivariate statistical techniques for investigating climate change and anthropogenic effects on surface water quality assessment: case study of Zohreh river, Hendijan, Iran

2021 ◽  
Vol 11 (6) ◽  
Author(s):  
Jalal Valiallahi ◽  
Saideh Khaffaf Roudy
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Monitoring Program ◽  
T Test ◽  
Total Variance ◽  
Quality Data ◽  
Water Quality Data ◽  
Significant Difference ◽  
The Mean

AbstractIn the present study, evaluation of spatial variations and interpretation of Zohrehh River water quality data were made by using multivariate analytical techniques including factor analysis and cluster analysis also the Arc GIS® software was used. The research method was formulated to achieve objectives herein, including field observation, numerical modeling, and laboratory analyses. The results showed that dataset consisted of 11,250 observations of seven-year monitoring program (measurement of 15 variables at 3 main stations from April 2010 to March 2017). Factor analysis with principal component analysis extraction of the dataset yielded seven varactors contributing to 82% of total variance and evaluated the incidence of each varactor on the total variance. The results of cluster analysis became complete with t-test and made water quality comparison between two clusters possible. Results of factor analysis were employed to facilitate t-test analysis. The t-test revealed the significant difference in a confidence interval of 95% between the mean of calculated varactors 1, 2, 6 and 7 between two clusters, but there was no significant difference in the mean of other varactors 3, 4 and 5 between two groups. The result shows the effect of agricultural fertilizers on stations located at downstream of the ASK dam.

scholarly journals Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

scholarly journals Surface Water Quality Assessment of Wular Lake, A Ramsar Site in Kashmir Himalaya, Using Discriminant Analysis and WQI

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Salim Aijaz Bhat ◽  
Ashok K. Pandit
Keyword(s):  
Water Quality ◽  
Discriminant Analysis ◽  
Quality Data ◽  
Stepwise Discriminant Analysis ◽  
Multivariate Techniques ◽  
Kashmir Himalaya ◽  
Water Quality Data ◽  
Data Set ◽  
Total N ◽  
The Mean

Multivariate techniques, discriminant analysis, and WQI were applied to analyze a water quality data set including 27 parameters at 5 sites of the Lake Wular in Kashmir Himalaya from 2011 to 2013 to investigate spatiotemporal variations and identify potential pollution sources. Spatial and temporal variations in water quality parameters were evaluated through stepwise discriminant analysis (DA). The first spatial discriminant function (DF) accounted for 76.5% of the total spatial variance, and the second DF accounted for 19.1%. The mean values of water temperature, EC, total-N, K, and silicate showed a strong contribution to discriminate the five sampling sites. The mean concentration of NO2-N, total-N, and sulphate showed a strong contribution to discriminate the four sampling seasons and accounted for most of the expected seasonal variations. The order of major cations and anions was Ca2+>Mg2+> Na+>K+ and Cl->SO42->SiO22- respectively. The results of water quality index, employing thirteen core parameters vital for drinking water purposes, showed values of 49.2, 46.5, 47.3, 40.6, and 37.1 for sites I, II, III, IV, and V, respectively. These index values reflect that the water of lake is in good condition for different purposes but increased values alarm us about future repercussions.

A water quality index for contact recreation in New Zealand

2001 ◽  
Vol 43 (5) ◽  
pp. 285-292 ◽  
Author(s):  
J. W. Nagels ◽  
R. J. Davies-Colley ◽  
D. G. Smith
Keyword(s):  
Water Quality ◽  
New Zealand ◽  
Water Quality Index ◽  
Quality Index ◽  
Quality Data ◽  
Water Quality Data ◽  
E Coli ◽  
The Mean ◽  
Index Value ◽  
Contact Recreation

We surveyed the opinions of 16 water quality experts in order to develop a water quality index for contact recreation in freshwaters in New Zealand. The index was developed by postal surveys using the Delphi method, involving feedback of information to the panel members at each iteration. Determinands selected for use in the index were as follows: faecal bacterial indicators (faecal coliforms or E. coli), pH, Munsell colour, visual clarity indicators (black disc visibility or turbidity), and nutrients promoting nuisance growths (filtered BOD5, and dissolved forms of phosphorus and nitrogen). “Sub-index” curves relating suitability-for-use to these water quality determinands have been developed. The mean (“consensus”) sub-index curves can be used to interpret water quality data in terms of suitability-for-use scores. We advocate using the lowest suitability-for-use score for a water as its overall index value for contact recreation. Thus the water body's suitability-for-use is determined by its “poorest”characteristic. The index is now ready to be tested by water managers for its utility in state-of-environment reporting.

scholarly journals Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

scholarly journals Modelling lake trophic state: A random forest approach

2015 ◽  
Author(s):  
Jeffrey W Hollister ◽  
W. Bryan Milstead ◽  
Betty J. Kreakie
Keyword(s):  
Water Quality ◽  
Random Forests ◽  
Trophic State ◽  
Mean Squared Error ◽  
Quality Data ◽  
Full Model ◽  
Water Quality Data ◽  
Squared Error ◽  
The Mean

Productivity of lentic ecosystems is well studied and it is widely accepted that as nutrient inputs increase, productivity increases and lakes transition from lower trophic state (e.g. oligotrophic) to higher trophic states (e.g. eutrophic). These broad trophic state classifications are good predictors of ecosystem condition, services, and disservices (e.g. recreation, aesthetics, and harmful algal blooms). While the relationship between nutrients and trophic state provides reliable predictions, it requires in situ water quality data in order to parameterize the model. This limits the application of these models to lakes with existing and, more importantly, available water quality data. To address this, we take advantage of the availability of a large national lakes water quality database (i.e. the National Lakes Assessment), land use/land cover data, lake morphometry data, other universally available data, and apply data mining approaches to predict trophic state. Using this data and random forests, we first model chlorophyll a, then classify the resultant predictions into trophic states. The full model estimates chlorophyll a with both in situ and universally available data. The mean squared error and adjusted R2 of this model was 0.09 and 0.8, respectively. The second model uses universally available GIS data only. The mean squared error was 0.22 and the adjusted R2 was 0.48. The accuracy of the trophic state classifications derived from the chlorophyll a predictions were 69% for the full model and 49% for the “GIS only” model. Random forests extend the usefulness of the class predictions by providing prediction probabilities for each lake. This allows us to make trophic state predictions and also indicate the level of uncertainity around those predictions. For the full model, these predicted class probabilites ranged from 0.42 to 1. For the GIS only model, they ranged from 0.33 to 0.96. It is our conclusion that in situ data are required for better predictions, yet GIS and universally available data provide trophic state predictions, with estimated uncertainty, that still have the potential for a broad array of applications. The source code and data for this manuscript are available from https://github.com/USEPA/LakeTrophicModelling.

Predicting Daily Maintenance Dose of Fluindione, an Oral Anticoagulant Drug

1996 ◽  
Vol 75 (05) ◽  
pp. 731-733 ◽  
Author(s):  
V Cazaux ◽  
B Gauthier ◽  
A Elias ◽  
D Lefebvre ◽  
J Tredez ◽  
...  
Keyword(s):  
Effective Dose ◽  
Maintenance Dose ◽  
Successive Approximations ◽  
Hemorrhagic Complication ◽  
Simple Method ◽  
Individual Variations ◽  
Anticoagulant Drug ◽  
The Mean ◽  
Mean Time ◽  
Daily Maintenance Dose

SummaryDue to large inter-individual variations, the dose of vitamin K antagonist required to target the desired hypocoagulability is hardly predictible for a given patient, and the time needed to reach therapeutic equilibrium may be excessively long. This work reports on a simple method for predicting the daily maintenance dose of fluindione after the third intake. In a first step, 37 patients were delivered 20 mg of fluindione once a day, at 6 p.m. for 3 consecutive days. On the morning of the 4th day an INR was performed. During the following days the dose was adjusted to target an INR between 2 and 3. There was a good correlation (r = 0.83, p<0.001) between the INR performed on the morning of day 4 and the daily maintenance dose determined later by successive approximations. This allowed us to write a decisional algorithm to predict the effective maintenance dose of fluindione from the INR performed on day 4. The usefulness and the safety of this approach was tested in a second prospective study on 46 patients receiving fluindione according to the same initial scheme. The predicted dose was compared to the effective dose soon after having reached the equilibrium, then 30 and 90 days after. To within 5 mg (one quarter of a tablet), the predicted dose was the effective dose in 98%, 86% and 81% of the patients at the 3 times respectively. The mean time needed to reach the therapeutic equilibrium was reduced from 13 days in the first study to 6 days in the second study. No hemorrhagic complication occurred. Thus the strategy formerly developed to predict the daily maintenance dose of warfarin from the prothrombin time ratio or the thrombotest performed 3 days after starting the treatment may also be applied to fluindione and the INR measurement.

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