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Assessment of predictive models for chlorophyll-a concentration of a tropical lake.

BACKGROUND: This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstra...

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Main Authors: Syed Ahmad Abdul Rahman, Sharifah Mumtazah, Malek, Sorayya, Kashmir Singh, Sarinder Kaur, Milow, Pozi, Salleh, Aishah
Format: Article
Published: 2011
Online Access:http://psasir.upm.edu.my/id/eprint/23246/
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spelling my.upm.eprints.232462014-11-04T05:13:44Z http://psasir.upm.edu.my/id/eprint/23246/ Assessment of predictive models for chlorophyll-a concentration of a tropical lake. Syed Ahmad Abdul Rahman, Sharifah Mumtazah Malek, Sorayya Kashmir Singh, Sarinder Kaur Milow, Pozi Salleh, Aishah BACKGROUND: This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. RESULTS: Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task. CONCLUSIONS: Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR. 2011 Article PeerReviewed Syed Ahmad Abdul Rahman, Sharifah Mumtazah and Malek, Sorayya and Kashmir Singh, Sarinder Kaur and Milow, Pozi and Salleh, Aishah (2011) Assessment of predictive models for chlorophyll-a concentration of a tropical lake. BMC Bioinformatics, 12 (13). pp. 1-11. ISSN 1471-2105 10.1186/1471-2105-12-S13-S12
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description BACKGROUND: This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes. RESULTS: Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task. CONCLUSIONS: Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.
format Article
author Syed Ahmad Abdul Rahman, Sharifah Mumtazah
Malek, Sorayya
Kashmir Singh, Sarinder Kaur
Milow, Pozi
Salleh, Aishah
spellingShingle Syed Ahmad Abdul Rahman, Sharifah Mumtazah
Malek, Sorayya
Kashmir Singh, Sarinder Kaur
Milow, Pozi
Salleh, Aishah
Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
author_facet Syed Ahmad Abdul Rahman, Sharifah Mumtazah
Malek, Sorayya
Kashmir Singh, Sarinder Kaur
Milow, Pozi
Salleh, Aishah
author_sort Syed Ahmad Abdul Rahman, Sharifah Mumtazah
title Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
title_short Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
title_full Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
title_fullStr Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
title_full_unstemmed Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
title_sort assessment of predictive models for chlorophyll-a concentration of a tropical lake.
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/23246/
_version_ 1643828000771276800
score 13.211869

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