title: Assessment of predictive models for chlorophyll-a concentration of a tropical lake.
abstract: 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.
authors.affiliation: Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia. sorayya@um.edu.my
authors.forename: Sorayya
authors.initials: S
authors.lastname: Malek
authors.forename: Sharifah Mumtazah
authors.initials: SM
authors.lastname: Syed Ahmad
authors.forename: Sarinder Kaur Kashmir
authors.initials: SK
authors.lastname: Singh
authors.forename: Pozi
authors.initials: P
authors.lastname: Milow
authors.forename: Aishah
authors.initials: A
authors.lastname: Salleh
mesh_terms: D002734:Chlorophyll / Q000032:analysis*; D017753:Ecosystem; D005068:Eutrophication; D060106:Lakes / Q000382:microbiology; D016014:Linear Models; D008296:Malaysia; D008954:Models, Biological; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000032:analysis
chemical_list: D002734:Chlorophyll; D009584:Nitrogen