Application of the active learning method to the retrieval of pigment from spectral remote sensing reflectance data

Shahraiyni, Hamid Taheri; Shouraki, Saeed Bagheri; Fell, Frank; Schaale, M.; Fischer, Jürgen; Tavakoli, A.; Preusker, R.; Tajrishy, Masoud; Vatandoust, M.; Khodaparast, Hamed Haddad

doi:10.1080/01431160802448927

Cited by 13 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar findings have been presented by Bagheri Shouraki and Honda (1999). Taheri Shahraiyni et al (2009) demonstrated that the optimum points for fuzzy dividing are the first and third quarters of data hence according to Fig. 7 and the results of Taheri Shahraiyni et al (2009), the first and third quarters of data were selected as fuzzy dividing points in this study.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the appropriate points for fuzzy dividing can be calculated by inBrought to you by | MIT Libraries Authenticated Download Date | 5/10/18 10:26 AM vestigating various alternatives to select the most appropriate one. Bagheri Shouraki and Honda (1999) and Taheri Shahraiyni et al (2009) showed that the first and the third quantiles of data are the best dividing points. In this study, firstly the ALM was applied to input set and appropriate fuzzy dividing points were determined.…”

Section: Alm Modelingmentioning

confidence: 99%

“…Although, ALM implements crisp or fuzzy dividing methods, but fuzzy dividing and modeling methods can improve the ALM performance (Taheri et al, 2009).…”

Section: Fuzzy Dividingmentioning

confidence: 99%

“…ANNs), the ALM does not need initial parameters to start the training and thus it does not repeat the training. Hence the ALM training is easy, straightforward, and time efficient (Taheri Shahraiyni et al, 2009;Taheri Shahraiyni, 2010). When we divided the domain of a variable fuzzily, some of the data were shared in small and large parts of the variable domain.…”

Section: Alm Modelingmentioning

confidence: 99%

See 3 more Smart Citations

Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Shahraiyni¹,

Ghafouri²,

Shouraki³

et al. 2012

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

In this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic Algorithm (GA), as a well known datadriven model for long term simulation of daily streamflow in Karoon River. The daily discharge data from 1991 to 1996 and from 1996 to 1999 were utilized for training and testing of the models, respectively. Values of the Nash-Sutcliffe, Bias, R 2 , MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m 3 s -1 , 0.81, 12.9%, and 1.9%, respectively. Following the same order of parameters, these criteria for optimized SVM model were 0.8, -10.7 m 3 s -1 , 0.81, 7.3%, and -3.6%, respectively. The results show appropriate and acceptable simulation by ALM and optimized SVM. Optimized SVM is a well-known method for runoff simulation and its capabilities have been demonstrated. Therefore, the similarity between ALM and optimized SVM results imply the ability of ALM for runoff modeling. In addition, ALM training is easier and more straightforward than the training of many other data driven models such as optimized SVM and it is able to identify and rank the effective input variables for the runoff modeling. According to the results of ALM simulation and its abilities and properties, it has merit to be introduced as a new modeling method for the runoff modeling. Cieľom štúdie bolo porovnať možnosti dlhodobej simulácie denných prietokov v rieke Karoon pomocou novovyvinutej fuzzy metódy aktívneho učenia (Active Learning Method -ALM) a známej metódy vektormi podporených strojov (Support Vector Machine -SVM), optimalizovanej genetickým algoritmom (GA). Na tréning a testovanie modelov boli použité časové rady denných prietokov za obdobie rokov 1991 až 1996 a 1996 až 1999. Hodnoty parametrov Nash-Sutcliffe, Bias, R 2 , MPAE a PTVE pre model ALM boli 0,81; 5,5 m 3 s -1 ; 0,81; 12,9% a 1,9%. Parametre v tom istom poradí pre model SVM boli 0,8 -10,7 m 3 s -1 , 0,81; 7,3%; a -3,6%. Z výsledkov simulácií vyplýva, že aplikáciou metód ALM a SVM možno získať porovnateľné a akceptovateľné výsledky. Podobnosť výsledkov medzi ALM a SVM implikuje vhodnosť novovyvinutej metódy ALM pre simuláciu odtoku. Tréning ALM je ľahší a jednoduchší ako je tréning ďalších dátami riadených modelov podobného typu. Navyše algoritmus ALM je schopný identifikovať a zoradiť efektívne vstupné premenné pre modelovanie odtoku. Na základe dosiahnutých výsledkov možno metódu ALM zaradiť medzi nové, alternatívne metódy modelovania odtoku.KĽÚČOVÉ SLOVÁ: modelovanie odtoku, metóda aktívneho učenia (ALM), metóda vektormi podporených strojov (SVM), fuzzy modelovanie, genetický algoritmus, povodie rieky Karoon.Brought to you by | MIT Libraries Authenticated Download Date | 5/10/18 10:26 AM Comparison between active learning method and support vector machine for runoff modeling

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Alm Modelingmentioning

confidence: 99%

“…Although, ALM implements crisp or fuzzy dividing methods, but fuzzy dividing and modeling methods can improve the ALM performance (Taheri et al, 2009).…”

Section: Fuzzy Dividingmentioning

confidence: 99%

Section: Alm Modelingmentioning

confidence: 99%

See 2 more Smart Citations

Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Shahraiyni¹,

Ghafouri²,

Shouraki³

et al. 2012

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

show abstract

“…Machine-learning algorithms were introduced in image processing as powerful alternatives for the retrieval of water properties due to their ability to approximate a set of input data to the corresponding output and the limited assumptions required (Thiria et al, 1993 (Matarrese et al, 2008) and hybrid active learning models (Shahraiyni et al, 2009). These approaches appear to be robust to noise and to allow the application of complex bidirectional radiative transfer models providing stable numerical outputs.…”

Section: Special Issue Science Of the Total Environmentmentioning

confidence: 99%

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

Tyler

Hunter

Spyrakos

et al. 2016

Science of The Total Environment

121

View full text Add to dashboard Cite

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production.Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelfsea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable.

show abstract

Re-construction of the shut-down PM10 monitoring stations for the reliable assessment of PM10 in Berlin using fuzzy modelling and data transformation

Shahraiyni

Sodoudi

Kerschbaumer

et al. 2017

Environ Monit Assess

View full text Add to dashboard Cite

A dense monitoring network is vital for the reliable assessment of PM10 in different parts of an urban area. In this study, a new idea is employed for the re-construction of the 20 shut-down PM10 monitoring stations of Berlin. It endeavours to find the non-linear relationship between the hourly PM10 concentration of both the still operating and the shut-down PM10 monitoring stations by using a fuzzy modelling technique, called modified active learning method (MALM). In addition, the simulations were performed by using not only raw PM10 databases but also log-transformed PM10 databases for skewness reduction. According to the results of hourly PM10 simulation (root mean square error about 13.0 μg/m, correlation coefficient 0.88), the shut-down stations have been appropriately simulated and the idea of dense monitoring network development by the re-construction of the shut-down stations was realised. The results of simulations using raw and log-transformed databases showed that data transformation has no significant effect on the performance of MALM in the simulation of shut-down PM10 stations. By the combination of the 11 still operating stations and the 20 re-constructed stations, a dense monitoring network was generated for Berlin and was utilised for the calculation of the reliable monthly and mean annual PM10 concentration for five different PM10 zones in Berlin (the suburban-background, urban-background, urban-traffic, rural-background and suburban-traffic areas). The results showed that the mean annual concentration of PM10 at the five zones increased by about 13.0% in 2014 (26.3 μg/m) in comparison with 2013 (23.3 μg/m). Furthermore, the mean annual concentration of PM10 in the traffic lanes of the suburban (2013 25.0 μg/m, 2014 26.9 μg/m) and urban (2013 27.7 μg/m, 2014 31.3 μg/m) areas is about 14 and 20% higher than the PM10 concentration of suburban-background (2013 21.3 μg/m, 2014 24.5 μg/m) and urban-background (2013 23.0 μg/m, 2014 26.1 μg/m) areas, respectively.

show abstract

Application of the active learning method to the retrieval of pigment from spectral remote sensing reflectance data

Cited by 13 publications

References 17 publications

Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

Re-construction of the shut-down PM10 monitoring stations for the reliable assessment of PM10 in Berlin using fuzzy modelling and data transformation

Contact Info

Product

Resources

About