scholarly journals Application of Spectral Index-Based Logistic Regression to Detect Inland Water in the South Caucasus

2021 ◽  
Vol 13 (24) ◽  
pp. 5099
Author(s):  
James Worden ◽  
Kirsten M. de Beurs ◽  
Jennifer Koch ◽  
Braden C. Owsley
Keyword(s):  
Logistic Regression ◽  
Surface Water ◽  
Training Data ◽  
South Caucasus ◽  
Joint Research ◽  
Error Matrix ◽  
Caucasus Region ◽  
Small Water ◽  
Water Detection ◽  
Global Water

The Caucasus is a diverse region with many climate zones that range from subtropical lowlands to mountainous alpine areas. The region is marked by irrigated croplands fed by irrigation canals, heavily vegetated wetlands, lakes, and reservoirs. In this study, we demonstrate the development of an improved surface water map based on a global water dataset to get a better understanding of the spatial distribution of small water bodies. First, we used the global water product from the European Commission Joint Research Center (JRC) to generate training data points by stratified random sampling. Next, we applied the optimal probability cut-off logistic regression model to develop surface water datasets for the entire Caucasus region, covering 19 Landsat tiles from May to October 2019. Finally, we used 6745 manually classified points (3261 non-water, 3484 water) to validate both the newly developed water dataset and the JRC global surface water dataset using an estimated proportion of area error matrix to evaluate accuracy. Our approach produced surface water extent maps with higher accuracy (89.2%) and detected 392 km2 more water than the global product (86.7% accuracy). We demonstrate that the newly developed method enables surface water detection of small ponds and lakes, flooded agricultural fields, and narrow irrigation channels, which are particularly important for mosquito-borne diseases.

scholarly journals Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

2018 ◽  
Vol 22 (8) ◽  
pp. 4349-4380 ◽  
Author(s):  
Andrew Ogilvie ◽  
Gilles Belaud ◽  
Sylvain Massuel ◽  
Mark Mulligan ◽  
Patrick Le Goulven ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Landsat Imagery ◽  
Water Monitoring ◽  
Water Bodies ◽  
Small Lakes ◽  
Small Water ◽  
Water Detection ◽  
Automated Method ◽  
Small Water Bodies

Abstract. Hydrometric monitoring of small water bodies (1–10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies, but the influence of their limited surface areas, vegetation growth, and rapid flood dynamics on long-term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimize the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these small water bodies. Extensive hydrometric field data (1999–2014) for seven small reservoirs within the Merguellil catchment in central Tunisia and SPOT imagery are used to calibrate the method and explore its limits. The Modified Normalised Difference Water Index (MNDWI) is shown out of six commonly used water detection indices to provide high overall accuracy and threshold stability during high and low floods, leading to a mean surface area error below 15 %. Applied to 546 Landsat 5, 7, and 8 images over 1999–2014, the method reproduces surface water extent variations across small lakes with high skill (R2=0.9) and a mean root mean square error (RMSE) of 9300 m2. Comparison with published global water datasets reveals a mean RMSE of 21 800 m2 (+134 %) on the same lakes and highlights the value of a tailored MNDWI approach to improve hydrological monitoring in small lakes and reduce omission errors of flooded vegetation. The rise in relative errors due to the larger proportion and influence of mixed pixels restricts surface water monitoring below 3 ha with Landsat (Normalised RMSE = 27 %). Interferences from clouds and scan line corrector failure on ETM+ after 2003 also decrease the number of operational images by 51 %, reducing performance on lakes with rapid flood declines. Combining Landsat observations with 10 m pansharpened Sentinel-2 imagery further reduces RMSE to 5200 m2, displaying the increased opportunities for surface water monitoring in small water bodies after 2015.

scholarly journals Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

2018 ◽  
Author(s):  
Andrew Ogilvie ◽  
Gilles Belaud ◽  
Sylvain Massuel ◽  
Mark Mulligan ◽  
Patrick Le Goulven ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Landsat Imagery ◽  
Water Monitoring ◽  
Water Bodies ◽  
Small Lakes ◽  
Small Water ◽  
Water Detection ◽  
Automated Method ◽  
Small Water Bodies

Abstract. Hydrometric monitoring of small water bodies (1–10 ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies but the influence of their limited surface areas, vegetation growth and rapid flood dynamics on long term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimise the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these smallest water bodies. Extensive hydrometric field data (1999–2014) for 7 small reservoirs within the Merguellil catchment in Central Tunisia are used to calibrate the method and explore its limits. MNDWI is shown out of six commonly used water detection indices to provide high overall accuracy and threshold stability during high and low floods, leading to a mean surface area error below 15 %. Applied to 546 Landsat 5, 7 and 8 images over 1999–2014, the method reproduces surface water extent variations across small lakes with high skill (R2 = 0.9) and mean RMSE of 9 300 m2. Comparison with published global water data sets reveals a mean RMSE of 21 800 m2 (+134 %) on the same lakes and highlights the value of a tailored MNDWI approach to improve hydrological monitoring in small lakes and reduce omission errors of flooded vegetation. The rise in relative errors due to the larger proportion and influence of mixed pixels restricts surface water monitoring below 3 ha with Landsat (NRMSE = 27 %). Interferences from clouds & scan line corrector failure on ETM+ after 2003 also decrease the number of operational images by 51 %, reducing performance on lakes with rapid flood declines.

Conclusion

2017 ◽  
Author(s):  
Mehran Kamrava
Keyword(s):  
Soft Power ◽  
The South ◽  
South Caucasus ◽  
Caucasus Region ◽  
Long Run ◽  
Middle Powers

As middle powers with regional aspirations, Iran and Turkey see the South Caucasus region as an ideal arena for expanding their reach and influence. As post-sanctions Iran finds greater space for diplomacy and trade, the ensuing competition between the two neighboring countries is likely to intensify in the coming years. For both states, trade and soft power are the most viable tools for expanding their influence. In the long run, the competition in trade is only likely to benefit the three states of the South Caucasus. But it is also likely to keep the multiple conflicts that have ravaged the region over the last several decades — especially between Armenia and Azerbaijan, Russia and Georgia, and even the historic animosity between Turkey and Armenia — frozen and without a solution in sight.

Pipeline Politics in Iran, Turkey, and the South Caucasus

2017 ◽  
Author(s):  
Mahmood Monshipouri
Keyword(s):  
Local Governments ◽  
Oil And Gas ◽  
Complex Dynamics ◽  
The South ◽  
South Caucasus ◽  
The Caspian Sea ◽  
Economic Interests ◽  
Oil Companies ◽  
Caucasus Region ◽  
Territorial Conflicts

The relationship between Iran, Turkey and the South Caucasus states have been influenced by an array of geopolitical, strategic, cultural, and economic factors. The competition between Iran and Turkey and their roles in the South Caucasus are best defined by traditional balance-of-power relations and the broader context of the post-Soviet era. This chapter unpacks the complex dynamics of pipeline politics in the South Caucasus region by underlying the need to understand the “Great Power Game” involving geostrategic and geo-economic interests of local governments, regional actors, global powers, and international oil companies. The larger focus turns on underscoring the importance of the region’s large oil and gas reserves; its land connection between the Caspian Sea, South Caucasus, and Europe; and its long-standing territorial conflicts in the post-Soviet era. Iran and Turkey have fought for influence in the South Caucasus while maintaining relatively good bilateral relationships in the region.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals 630. A 5-mRNA host response whole-blood classifier trained using patients with non-COVID-19 viral infections accurately predicts severity of COVID-19

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S375-S376
Author(s):  
ljubomir Buturovic ◽  
Purvesh Khatri ◽  
Benjamin Tang ◽  
Kevin Lai ◽  
Win Sen Kuan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Respiratory Failure ◽  
Viral Infections ◽  
Lamp Assay ◽  
Training Data ◽  
Diagnostic Tools ◽  
Severe Respiratory Failure ◽  
Proof Of Concept ◽  
Risk Of Death ◽  
Qpcr Platform

Abstract Background While major progress has been made to establish diagnostic tools for the diagnosis of SARS-CoV-2 infection, determining the severity of COVID-19 remains an unmet medical need. With limited hospital resources, gauging severity would allow for some patients to safely recover in home quarantine while ensuring sicker patients get needed care. We discovered a 5 host mRNA-based classifier for the severity of influenza and other acute viral infections and validated the classifier in COVID-19 patients from Greece. Methods We used training data (N=705) from 21 retrospective clinical studies of influenza and other viral illnesses. Five host mRNAs from a preselected panel were applied to train a logistic regression classifier for predicting 30-day mortality in influenza and other viral illnesses. We then applied this classifier, with fixed weights, to an independent cohort of subjects with confirmed COVID-19 from Athens, Greece (N=71) using NanoString nCounter. Finally, we developed a proof-of-concept rapid, isothermal qRT-LAMP assay for the 5-mRNA host signature using the QuantStudio 6 qPCR platform. Results In 71 patients with COVID-19, the 5 mRNA classifier had an AUROC of 0.88 (95% CI 0.80-0.97) for identifying patients with severe respiratory failure and/or 30-day mortality (Figure 1). Applying a preset cutoff based on training data, the 5-mRNA classifier had 100% sensitivity and 46% specificity for identifying mortality, and 88% sensitivity and 68% specificity for identifying severe respiratory failure. Finally, our proof-of-concept qRT-LAMP assay showed high correlation with the reference NanoString 5-mRNA classifier (r=0.95). Figure 1. Validation of the 5-mRNA classifier in the COVID-19 cohort. (A) Expression of the 5 genes used in the logistic regression model in patients with (red) and without (blue) mortality. (B) The 5-mRNA classifier accurately distinguishes non-severe and severe patients with COVID-19 as well as those at risk of death. Conclusion Our 5-mRNA classifier demonstrated very high accuracy for the prediction of COVID-19 severity and could assist in the rapid, point-of-impact assessment of patients with confirmed COVID-19 to determine level of care thereby improving patient management and healthcare burden. Disclosures ljubomir Buturovic, PhD, Inflammatix Inc. (Employee, Shareholder) Purvesh Khatri, PhD, Inflammatix Inc. (Shareholder) Oliver Liesenfeld, MD, Inflammatix Inc. (Employee, Shareholder) James Wacker, n/a, Inflammatix Inc. (Employee, Shareholder) Uros Midic, PhD, Inflammatix Inc. (Employee, Shareholder) Roland Luethy, PhD, Inflammatix Inc. (Employee, Shareholder) David C. Rawling, PhD, Inflammatix Inc. (Employee, Shareholder) Timothy Sweeney, MD, Inflammatix, Inc. (Employee)

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 212-213
Keyword(s):  
Logistic Regression ◽  
Private Information ◽  
Statistical Models ◽  
Heart Sound ◽  
Information Leakage ◽  
Scientific Data ◽  
Training Data ◽  
Genotype Data ◽  
Scientific Data Management ◽  
Heart Sound Segmentation

Agarwal V, Podchiyska T, Banda JM, Goel V, Leung TI, Minty EP, Sweeney TE, Gyang E, Shah NH. Learning statistical models of phenotypes using noisy labeled training data. J Am Med Inform Assoc 2016;23(6):1166-73 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocw028 Harmanci A, Gerstein M. Quantification of private information leakage from phenotype-genotype data: linking attacks. Nat Methods 2016;13(3):251-6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4834871/ Pfiffner PB, Pinyol I, Natter MD, Mandl KD. C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR. PloS One 2016;11(3):e0152722 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816293/ Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJ, Groth P, Goble C, Grethe JS, Heringa J, ‘t Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175/ Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 2016 Apr;63(4):822-32

scholarly journals Changes in ethnic structure in the Caucasus region since the end of the 1980’s: primary statistical analysis

Geografie ◽  
2009 ◽  
Vol 114 (2) ◽  
pp. 130-144
Author(s):  
Libor Jelen
Keyword(s):  
Statistical Analysis ◽  
Population Growth ◽  
South Caucasus ◽  
The Caucasus ◽  
Urbanization Level ◽  
Caucasus Region ◽  
The North ◽  
Factors Influencing ◽  
Regional Population ◽  
Political Units

The article deals with changes in ethnic structure in 13 political units of the North and the South Caucasus resulting from societal processes going on after the last 1989 Soviet census and illustrated by the outcome of censuses held in 1999–2005. The study deals with changes in population share of titular groups, Russians and other ethnic groups, with changing urbanization level and general regional population growth. It also makes an assessment of substantial changes in the ethnic structure in selected territories in connection with political and economical factors influencing the post-1989 development of the region and its ethno-territorial entities.

scholarly journals Understanding covariate shift in model performance

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 597 ◽  
Author(s):  
Georgia McGaughey ◽  
W. Patrick Walters ◽  
Brian Goldman
Keyword(s):  
Logistic Regression ◽  
Model Performance ◽  
Training Data ◽  
Obvious Advantage

Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets.

scholarly journals THE HISTORY OF THE GREAT SELJUK STATE IN AZERBAIJANI HISTORIOGRAPHY

Globus ◽  
2020 ◽  
Author(s):  
M. Bayramov
Keyword(s):  
Middle Ages ◽  
Cultural Development ◽  
The Political ◽  
Special Role ◽  
Military Operations ◽  
South Caucasus ◽  
The Caucasus ◽  
Caucasus Region ◽  
The People ◽  
History Of

The history of the Seljuk state, which played a significant role in the political, economic and cultural life of the Near and Middle East in the Middle Ages, is one of the most actual problems in Azerbaijani historiography. As it is known, after the establishment of the Seljuk state by the Turks, their main policy was to advance to the west, to seize Anatolia, to turn Anatolia into Turkish lands. The Caucasus region was the gateway to Anatolia. That is why the Caucasus, as well as Azerbaijan was of great military-strategic importance for the Seljuks. After the Dandanekan victory, it was decided at the Congress in Merv to launch new military operations to the East and West. The main target of the attack was Iran, Byzantium and the South Caucasus, because these countries were in political disarray and unable to resist them. Seljuk troops advancing on the Caucasus soon subjugated the local feudal states. The people of Azerbaijan, who have been under the rule of the Seljuk state for more than a century, have played a special role in the political and cultural development of the Seljuk state. However, this problem in national historiography has been a separate research topic only in the second half of the 20th century, which has long been out of sight. The present article is devoted to the study of Seljuk state in Azerbaijani historiography. The article studies the works of prominent Azerbaijani historians Z. Bunyadov, R. Huseynov, N. Akhundova, N.Aliyeva, Sh.Mustafayev, I.Hajiyev, T.Dostiyev and others, who have done research in this area since the second half of the twentieth to the first decade of the twenty-first century and their role in the study of the history of the great state in the medieval Muslim East, the Seljuk State, has been defined

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