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

- Sorayya Malek†
^{1}Email author, - Sharifah Mumtazah Syed Ahmad†
^{2}, - Sarinder Kaur Kashmir Singh†
^{1}, - Pozi Milow†
^{1}and - Aishah Salleh†
^{1}

**12(Suppl 13)**:S12

https://doi.org/10.1186/1471-2105-12-S13-S12

© Malek et al; licensee BioMed Central Ltd. 2011

**Published: **30 November 2011

## 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.

## Keywords

## Background

Eutrophication of lakes is a common global concern in lakes and reservoir. Malaysian lakes and reservoirs are also facing the same problem, as the current status of eutrophication is indicated to be more than 60% [1, 2]. The adverse effects of eutrophication amongst are deterioration of water quality for human utilization, limitation of recreational usage and depletion of dissolved oxygen below acceptable level which induces reductions in specific fish and other animal populations [3]. Eutrophication promotes algae bloom hence algal biomass can be used as a good indicator of eutrophication status in lakes and reservoir around the world [4]. Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae [5]. Various models have been developed to estimate the concentration of chlorophyll-a in temperate waters such as Artificial Neural Networks (ANN), Fuzzy Logic (FL), Hybrid Evolutionary Algorithm (HEA) and Multiple Linear Regression (MLR).

Artificial neural network (ANN) model are highly flexible function approximators that can be used to model non-linear relationship. ANN models have been successfully applied to predict chlorophyll-a concentration at temperate water bodies [6–10]. Similarly, FL models can be used to model non-linear relationships. Unlike ANN, FL models can provide insight into their own operation because the fuzzy rules provide an easily understood and common sense description of the action of the FL system. FL model has been used to model eutrophication, in lakes, reservoirs, and coastal waters [11–14]. Hybrid Evolutionary Algorithm (HEA) can be used to model non- linear relationship as well. HEA uses genetic programming (GP) to generate the structure of the rule set and genetic algorithm (GA) for parameter optimization. HEA has been successfully applied to discover complex rule sets predicting the concentration of chlorophyll-a [15–17]. MLR had been used to predict chlorophyll-a concentrations based on limited number of parameters such as phosphorus and nitrogen concentrations by [3, 18, 19].

Comparison of the above models had been already carried out in eutrophication studies of temperate lakes [9–11, 17]. The performance criteria used to assess these models were root mean square error (RMSE) and correlation coefficient (r-value). In this study, area under the ROC curve (AUC) was used as an additional performance criterion. AUC was calculated by plotting ROC curves, which were two-dimensional graphs that visually depicted the performance and performance trade-off of a classification model [20]. ROC curves were originally designed as tools in communication theory to visually determine optimal operating points for signal discriminators [21]. ROC was used in this study as chlorophyll-a concentrations can be dichotomized. For examples, different levels of eutropihication in a lake might represent either acceptable or unacceptable water quality, or concentrations of algal in the ocean might be classified as blooms if they exceed a certain threshold [22].

The aim of this study was to assess the performance of four different models, namely RANN, FL, HEA and MLR to predict concentration of chlorophyll-a in a tropical lake. To date, there are no literatures reported that assess the performance of the four models in a single study. The advantage of using AUC as an additional performance criterion is also discussed.

## Methods

### Study site and data

Principal features of Putrajaya Lakes

Catchment area | Water level | Surface area | Storage volume | Average depth | Average catchment inflow | Average retention time | Circulation type |
---|---|---|---|---|---|---|---|

50.9 square kilometers | RL 21 meters | 400 Hectares | 26.5 million cubic meters | 6.6 meters | 200 million liters per day | 132 days | warm polymictic (non stratified shallow lake) |

Summary statistics of limnological parameters from 2001 - 2004

Variables | Min | Avg | Max |
---|---|---|---|

Water temperature (°C) | 28.42 | 30.29 | 32.33 |

pH | 6.16 | 7.40 | 8.44 |

Dissolved oxygen (DO) (mg/l) | 5.72 | 7.41 | 9.08 |

Secchi depth (m) | 0.30 | 1.03 | 1.75 |

Turbidity (NTU) | 3.50 | 13.68 | 53.10 |

Conductivity (Us/cm) | 60.00 | 92.52 | 189.00 |

Ammonia nitrogen (NH3-N)(mg/l) | 0.00 | 0.05 | 0.53 |

Nitrate nitrogen (NO3-N)(mg/l) | 0.00 | 1.18 | 4.82 |

Chemical oxygen demand (COD)(mg/l) | 2.00 | 18.61 | 79.00 |

Total suspended solids (TSS)(mg/l) | 0.00 | 4.88 | 46.00 |

Chlorophyll-a (mg/l) | 0.00 | 7.28 | 31.70 |

### Selection of input variables

In raw datasets, some variables have large variation or spread [26, 27]. Normalization of the raw datasets was therefore necessary to ensure that all values of the variables are within the same range. Input data was normalized to the range 0 to 1. Less importance is given to input selection methods in many of the ecological model development [28]. Presenting large number of input to ANN, increases the network size, which leads to increase of amount of data to estimate the connection weight and possible reduction of processing speed. Similar for FL models fewer variables lead to the reduction of the dimensions for the fuzzy association matrix, and hence provide for a simpler formulation of inference rules. Preferred method of input selection should be a combination of prior knowledge and analytical approaches [29]. The analytical approach used was this study is sensitivity analysis technique for RANN and FL models. This technique measures how much a small change in one of the independent variables affects the functional value [30]. It effectively measures change in a given input affects the output across the training data set. Inputs that have large sensitivities have more importance in the mapping and therefore are the ones we should keep. The inputs with small sensitivities can be discarded. This helps the training as it reduces the size of the network, decreases the cost of data collection, and improves performance.

However for MLR model, stepwise selection was used .This was because of high correlations among the input variables were indentified. Stepwise regression is a modification of forward selection that drops variables from the model if they lose their significance as other variables are added. This has resulted in selection of only dissolve oxygen as input variable for MLR model.

HEA model optimized variable selection using general genetic algorithm (GA) approach that leads to the inclusion of nitrate nitrogen, Secchi depth, dissolved oxygen and pH. Novel crossover operator based on the non convex linear combination of multiple parents during the recombination of the population is applied for parameters optimization [15].

### Model performance comparison criteria

The r value is a measure of correlation between the predicted and observed values of the independent variable. The r value indicates agreement between predicted and observed values but it does not indicate the performance of the models. Models are considered reliable when its predicted values correlate with observed values at r value of 0.5 or above.

In this study, AUC was calculated from ROC curve graphs. ROC curve is a graphical plot of sensitivity or true positive rate versus false positive rate. In order to plot the ROC curve the concentration of chlorophyll-a was scaled according to different eutrophication scales [31]: ultra-oligotrophic (<=1.00*μ* g/L), oligotrophic (1.00–2.50 *μ* g/L); mesotrophic (2.5–8.0*μ* g/L), eutrophic (8.0–25.0 *μ* g/L) and hypertrophic (>=25 *μ* g/L). The AUC was then calculated to determine model performance using the trapezoidal rule. Trapezoids are formed using the observed points as corners and computing the areas of these trapezoids and adding them up. Thresh-hold values of AUC is adopted from [32]. AUC ranges from 0 to 1, where a score > 0.9 indicates outstanding discrimination, a score between 0.8–0.9 is excellent, and a score > 0.7 is acceptable.

### RANN model development

In the study, RANN [33] was used for model development. It followed deterministic modelling approach where the system state at time (t) was measured by system state at time (t-1) and the copied weights of time ( t-1 ) was used as feedback input to determine weights at time t. RANN was a modification to feed-forward neural network structure. RANN network geometry was determined via trial and error. One hidden layer RANN with back-propagation through time learning was employed. It has been reported that only one hidden layer is needed to approximate any continuous function [34]. The geometry of the RANN model (using water temperature, pH, dissolved oxygen, Secchi depth, ammonia nitrogen and nitrate nitrogen data as inputs) was 6-4-l (number of inputs-number of hidden nodes-number of outputs).

Back – propagation through time (BPTT) [35] a learning algorithm for RANN network was used in this study. The Back-propagation through time (BPTT) algorithm is based on changing the network from a feedback system to feed-forward system by folding the network over time. The network uses momentum learning algorithm to determine the weights in the network. This algorithm is an improvement to the gradient-descent search, where previous increment to the weight is used to speed up and stabilize convergence. In momentum learning the equation to update the weights are as follows:

wij(n +1) = wij (n) + η∂i(n)xj(n) + α(wij (n) – wij(n-1)

Where α is the momentum constant. Weights adaptation using momentum learning is changed proportionally to how much they were updated in the last iteration. Momentum learning is a robust method to speed up learning, and it suitable as the default search rule for networks with nonlinearities [36].

The size of the steps taken in weight space during the learning phase is a function of a number of internal network parameters including the learning rate, momentum value, error function, epoch size and gain of the transfer function. Appropriate step sizes, and hence, appropriate combinations of network parameters in this study, are determined by trial and error. Change in algal biomass is known to be nonlinear process [37]. To introduce nonlinearity to system hyperbolic tangent function is used at as an activation function at the hidden layer and at the output layer.

f(x) = tanh(αx)

α is a slope parameter and is set to 0.7, learning rate of 0.01 was used . An epoch size used is 100.

Generalization ability of a network, as measured by the RMSE between the predicted and historical values of an independent test set, changes as training progresses is a function of the size of the steps taken in weight space . In order to optimize model performance, three data sets: are used for the RANN model development in this study a training set (dataset A), a test set (dataset B), and a validation set (dataset C). The test set was used to evaluate the generalization ability of the network. The validation set was used to assess the performance of the model once the training phase has been completed. The process of cross-validation removes the risk of the neural network memorizing the data [38].

### FL model development

The FL model used to model chlorophyll-a concentration was adopted from [14]. The model development constitutes three basic steps.

#### Data reduction

Variables used to develop the FL model were selected through sensitivity analysis generated from RANN. Fewer variables lead to the reduction of the dimensions for the fuzzy association matrix, and hence provide a simpler formulation of inference rules [13, 39]. Water temperature, pH, dissolved oxygen, Secchi depth, ammonia nitrogen and nitrate nitrogen was used as input variables.

#### Clustering of input and output data

Self-organizing maps (SOM) [40], an unsupervised learning method, was used to cluster input and output data. SOM is common technique used to analyse multi-dimensional data [27, 41] by providing for a reduced dimensional illustration. In this study, SOM was used to construct a two dimensional graph that described the clustering of input and output variables, whereby the clusters for input parameters can be mapped directly to the cluster of chlorophyll-a. In addition, the SOM is used to obtain mean values as well as 97.5% confidence interval bands of each cluster of model variables. If there was no statistically significant overlap between the clusters, the classification result is considered acceptable. The procedure of SOFM can be found in [42, 43]. Clusters obtained from SOM were used to extract information for defining the fuzzy membership functions and constructing the fuzzy inference rules.

#### Defining membership functions

Strategies for inference rules induction, extended case reasoning has been adopted in this study as explained in [14]. A total of 64 inference rules were generated for the chlorophyll-a FL model from the training dataset A. 16 rules were extracted to determine high algae biomass, 18 rules were determined for medium algae biomass and 30 rules extracted to determine low algae biomass. In order to conduct quantitative comparison, the developed models were tested with dataset B. This was to avoid biasness in results. The outputs for FL model were defuzzified by centre of gravity method and plotted together with observations.

### HEA model development

### MLR model development

In this study, MLR model was developed from dataset A using multiple regression procedure of the SPSS 17.0 software. The MLR model was developed using stepwise selection method as there exists high correlation between independent variables. Chlorophyll-a concentration was assigned as the dependent or criterion variable. The stepwise variable selection method identified dissolved oxygen as the independent variables. The developed models were tested with dataset B. This was to avoid biasness in results.

## Results

Result summary for chlorophyll-a prediction models

Models | RMSE | r | AUC |
---|---|---|---|

MLR | 4.60 | 0.5 | 0.76 |

FL | 4.49 | 0.6 | 0.84 |

RANN | 4.28 | 0.7 | 0.79 |

HEA | 4.27 | 0.7 | 0.82 |

HEA model was used to generate rules to discover relationship between cholorophyll-a concentrations and water quality parameters at Putrajaya Lake. The best rule set in terms of minimal testing error in 100 runs is given below.

IF ((((NO3N*126.117)>=5.273)OR(Secchi>154.513))AND(((NO3N*444.685)*(NO3N*13.361))<444.685))

THEN chlorophyll-a=(96.579/DO)

ELSE

Chlorophyll-a=(DO-ln(|((61.273/pH)-DO)|))

The MLR model equation using stepwise variable selection for Putrajaya Lake is as follows: Chlorophyll-a = -11.685 + 2.732.DO.

## Discussion

Same data set was used to assess the performances of the four models: RANN, FL, HEA and MLR. The RMSE value indicates that both HEA (4.27 ug/l) and RANN (4.28 ug/l) performed better than FL (4.49 ug/l) and MLR (4.60 ug/l). RMSE values obtained in this study were comparable with similar models developed for chlorophyll-a estimation at temperate lakes. FL model developed for temperate lakes recorded RMSE value of (7.0 ug/l) [13] and HEA model reported RMSE value of (39 – 87 ug/l) [15]. All the models are generally reliable as their predicted values correlate with observed values with r value of 0.5 or above. HEA and RANN produced similar performance when used for predicting phytoplankton biomass at temperate lakes [17]. Previous studies have also shown that RANN and FL performed better than MLR model [9–11]. This is consistent with findings of the present study.

Based on AUC rating in [32], FL (AUC value 0.84) and HEA (AUC value 0.82) can be categorized as excellent prediction models of chlorophyll-a concentration. RANN (AUC value 0.79) and MLR (AUC value 0.76) are categorized as acceptable models of chlorophyll-a concentration.

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. Better performance of FL model over RANN and HEA might be due to collapsing continuous response (chlorophyll-a concentration) into two values. Theoretically and empirically that AUC is a better measure for model evaluation than accuracy. RMSE meanwhile measures model accuracy. Many ecological responses are difficult to measure accurately and definitely. Therefore AUC is suitable for characterizing responses that are dichotomous such as lake eutrophication [44].

Dissolved oxygen was used to predict chlorophyll-a concentrations for the MLR model. Other variables were not used because they are highly correlated. Highly correlated variables were excluded stepwise during the process of constructing the MLR model. The use of MLR model to predict chlorophyll-a has serious drawbacks as the model is oversimplified. Eutrophication is a complex process with non linear relations between environmental variables and therefore cannot be explained with simplistic approach. Sensitivity analysis to select variables as used in RANN and FL determine the contributions of the independent variables and the way they act on the dependent variable. Sensitivity analysis adds strength to ANNs in their explanatory capacity. More importance is placed on variables that have large sensitivities. Variables with small sensitivities are discarded. This is important as the effect of presenting large number of input to ANN, increases the network size, which leads to increase of amount of data to estimate the connection weight and possible reduction of processing speed. Similarly for FL model large number of input causes difficulty in defining fuzzy members. Both RANN and FL models were developed using the final selected variables such as water temperature, Secchi depth, pH, ammonia nitrogen, dissolved oxygen and nitrate nitrogen. Chlorophyll-a concentration are related to algal biomass and concentration of chlorophyll-a in this study represent the five major division of algae that is Bacillariophyta, Chlorophyta, Cynanobacteria , Chrysophyta and Pyrropytha. It is well known that temperature can enhance phytoplankton growth rate [45, 46]. Cyanobacteria and Chlorophyta which comprises 28% and 26% of algae population are identified as major contributor of chlorophyll-a concentration in Putrajaya Lake. Cyanobacteria and Chlorophyta are known to prefer high water temperature [47–49]. Inability to grow at high pH is a characteristic of oligotrophic species mainly desmids which comprises of major population of algae at Putrajaya Lake [50]. It can be inferred that algae abundance at Putrajaya Lake are controlled by pH concentration. The nutrients, both ammonia nitrogen (NH3-N) and nitrate nitrogen (NO3-N) are among parameters selected by sensitivity analysis. Nutrients inputs into oligotrophic lakes often increase phytoplankton biomass and productivity [51]. Secchi depth is correlated with chlorophyll-a measurements. In many standing waters, determination of Secchi depth has been found to be a simple and reliable approach to monitoring changes in seasonal phytoplankton biomass. Meanwhile it is typical to find higher levels of oxygen in depths where larger concentrations of phytoplankton are found [52].

Even though ANN models are able to make perfect predictions and are recognised as powerful, they are considered to be ‘black-box’ in nature. Therefore explanatory methods such as FL and HEA have been adopted in this study with the idea to clarify the ‘black-box’ approach of ANNs. An FL approach proves to be a practical and successful technique when dealing with semi-qualitative knowledge and semiqualitative data [53] which is, for example, the case when trying to model algal biomass or algal blooms. However, the definition of appropriate membership functions and the induction of inference rules, common to any FL modelling approach, remain difficult, since these very much depend on specific knowledge and expertise of any particular ecologist [54]. HEA approach can overcome the limitation of FL and ANN approach. HEA allows discovery of predictive rule set in complex ecological data. The genetic algorithm used in HEA provides parameter optimization which resulted in the inclusion of nitrate nitrogen, Secchi depth, dissolved oxygen and pH for chlorophyll-a concentration estimation at Putrajaya Lake. The HEA rule sets discovered for chlorophyll-a concentrations at Putrajaya Lake is rather complex. The IF branch of the discovered rule set explains chlorophyll-a concentration can be determine by using dissolved oxygen when concentration of nitrate and Secchi depth are reported to be high. If this condition is not meet chlorophyll-a concentration is determine using the ELSE branch, where pH and dissolved oxygen is used. This can be justified by findings postulated in literature. Nutrients such as nitrates increase algae biomass. Concentrations of chlorophyll-a can be determined using dissolved oxygen as algal photosynthesis is usually the major supplier of oxygen to slow flowing water body. Dissolved oxygen and pH value in natural waters is primarily associated with photosynthesis [55].

## Conclusions

This paper presents an assessment of RANN, FL , HEA and MLR approaches in modelling chlorophyll-a of a tropical lake in Malaysia. In this study FL and HEA models produced promising results. FL and HEA approach prove to be practical and successful techniques when dealing with limited datasets of complex relationship without clear distinction of memberships. RANN model are data-driven models which is difficult to calibrate and requires a large number of datasets to perform accurate prediction. In this study FL, RANN and HEA give similar results and are potential algorithms to be deployed as water management tools as compared to MLR.

## Notes

## Declarations

### Acknowledgements

The referred data used for this study are courtesy of Mr. Akashah Hj. Majizat Environmental, of Lake and Wetland Division, City Planning Department, Perbadanan Putrajaya, Putrajaya, Malaysia.

This article has been published as part of *BMC Bioinformatics* Volume 12 Supplement 13, 2011: Tenth International Conference on Bioinformatics – First ISCB Asia Joint Conference 2011 (InCoB/ISCB-Asia 2011): Bioinformatics. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/12?issue=S13.

## Authors’ Affiliations

## References

- NAHRIM: A desktop Study on the Status of Lake Eutrophication in Malaysia, Final Report. Malaysia; 2005.Google Scholar
- Zati Zulkifli: National Overview: The Status of Lakes Eutrophication in Malaysia. In Colloquium on Lakes and Reservoir Management: Status and Issues. Putrajaya, Malaysia; 2007.Google Scholar
- Ryding S, Rast W: The Control of Eutrophication of lakes and Reservoirs. Parthenon Publishing Co., UNESCO; 1989.Google Scholar
- Chapman D: Water Quality Assessments A guide to the use of biota, sediments and water in environmental monitoring. 2nd edition. UNESCO/WHO/UNEP, E & FN SPON; 1996:59–126.View ArticleGoogle Scholar
- Padisák J: Phytoplankton. In The Lakes Handbook Volume 1 Limnology and Limnetic Ecology. Edited by: O’Sullivan PE, Reynolds CS. Blackwell Publishing; 2004:251–308.Google Scholar
- Lee JHW, Huang Y, Dickman M, Jayawardena AW: Neural network modelling of coastal algal blooms. Ecol.Model 2003, 159: 179–201. 10.1016/S0304-3800(02)00281-8View ArticleGoogle Scholar
- Hou GX, Song LR, Liu JT, Xiao BD, Liu YD: Modeling of Cyanobacterial blooms in Hypereutrophic Lake Dianchi, China. J. Freshwat. Ecol 2004, 19: 623–629. 10.1080/02705060.2004.9664743View ArticleGoogle Scholar
- Melesse AM, Krisnaswamy J, Zhang KQ: Modeling coastal eutrophication at Florida Bay using neural networks. J. Coast. Res 2008, 24('B):190–196.View ArticleGoogle Scholar
- Zeng Y, Yang ZF, Liu JF: Prediction of the concentration of chlorophyll- alpha for Liuhai urban lakes in Beijing City. J Environ Sci (China) 2006, 18(4):827–831.Google Scholar
- Karul C, Soyupak S, Cilesiz AF, Akbay N, Germen E: Case studies on the use of neural networks in eutrophication modelling. Ecol Model 2000, 134(2–3):145–152. 10.1016/S0304-3800(00)00360-4View ArticleGoogle Scholar
- Soyupak S, Chen DG: Fuzzy logic model to estimate seasonal pseudo steady state chlorophyll- a concentrations in reservoirs. Environ Monit Assess 2004, 9: 51–59.View ArticleGoogle Scholar
- Recknagel F, Petzoldt T, Jaeke O, Krusche F: Hybrid expert system DELAQUA—a toolkit for water quality control of lakes and reservoirs. Ecol Model 1994, 71: 17–36. 10.1016/0304-3800(94)90074-4View ArticleGoogle Scholar
- Chen Q: A robust fuzzy logic approach to modelling algal biomass. J Hydraulics Res 2004, 42(3):303–309. 10.1080/00221686.2004.9641198Google Scholar
- Chen Q, Mynett AE: Integration of data mining techniques with heuristic knowledge in a fuzzy logic modelling of eutrophication in Taihu Lake. Ecol Model 2003, 162: 55–67. 10.1016/S0304-3800(02)00389-7View ArticleGoogle Scholar
- Cao H, Recknagel F, Joob GJ, Kim DK: Discovery of predictive rule sets for chlorophyll-a dynamics in the Nakdong River (Korea) by means of the hybrid evolutionary algorithm HEA. Ecol Inform 2006, 43–53.Google Scholar
- Cao H, Recknagel F, Welk A, Kim B, Takamura N: Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication. In Ecol Inform. 2nd edition. Springer-Verlag Berlin, Heidelberg, New York; 2006:330–342.Google Scholar
- Recknagel F, Welk A, Cao H, Chan WS, Talib A: Rule-based agents for forecasting algal population dynamics in freshwater lakes discovered by hybrid evolutionary algorithms. Ecol Inform 2008, 46–54.Google Scholar
- Smith Shapiro: A retrospective Look at the Effects of Phosphorus Removal. In Inland Waters, USEPA, Office of Water Regulations and Standards. Washington, DC; 1981. EPA-440/5–81–010 EPA-440/5-81-010Google Scholar
- Canfield DC, Cooler DE, Haller WT, Watkins CE, Maceina MJ: Predictions of chlorophyll-a concentrations in Florida lakes: Importance of aquatic macrophytes. Can. Jour. Fish Aquatic Science 1984, 41: 409–501. 10.1139/f84-048View ArticleGoogle Scholar
- Flach P, Blockeel H, Ferri C, Hernandez-Orallo J, Struyf J: Decision support for data mining: Introduction to ROC analysis and its applications. Data mining and decision support: Aspects of integration and collaboration 2003, 81–90.View ArticleGoogle Scholar
- Egan JP: Signal detection theory and ROC analysis. Academic Press, New York; 1975.Google Scholar
- Murtaugh PA: The Statistical Evaluation of Ecological Indicators. Ecol Appl 1996, 6(1):132–139. 10.2307/2269559View ArticleGoogle Scholar
- Putrajaya Corporation: Wetlands Plants. Putrajaya Corporation, Selangor Darul Ehsan; 2000.Google Scholar
- WHO: UNEP/WHO/UNESCO/WMO Project on Global Environmental Monitoring. GEM Water Operational Guide 1987.Google Scholar
- American Public Health Association (APHA): Standard methods for the examination of water and waste water. 19th edition. American Water Works Association (AWWA) and Water Environment Federation APHA, Washington, DC; 1995.Google Scholar
- Haan CT: Statistical Methods in Hydrology. The Iowa State University Press; 1977.Google Scholar
- van den Boogaard HFP, Mynett AE, Ali MdS: Self organisation feature maps for the analysis of hydrological and ecological data sets. In Proceedings of Hydroinformatics Edited by: Babovic, V, Larsen LC. 1998.Google Scholar
- Maier HR, Dandy GC: Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications. Environ Modell Softw 2000, 15: 101–124. 10.1016/S1364-8152(99)00007-9View ArticleGoogle Scholar
- Lachtermacher , Fuller JD: Backpropagation in hydrological time series forecasting. In Stochastic and Statistical Methods in Hydrology and Environmental Engineering. Volume 3. Edited by: Hipel KW, MacLeod AI, Panu US, Singh VP. Time Series Analysis in Hydrology and Environmental Engineering; 1994:229–242.View ArticleGoogle Scholar
- Principe JC, Euliano NR, Lefebvre WC: Neural and Adaptive Systems: Fundamentals through Simulations. John Wiley & Sons; 1999:672.Google Scholar
- Wang XJ, Liu RM: Spatial analysis and eutrophication assessment for chlorophyll a in Taihu Lake. In Environ Monit Assess. Volume 101. Hill; 2005:167–174.Google Scholar
- Li Mingyang, Ju Yunwei, Sunil K, Thomas JS: Modeling potential habitats for alien species Dreissena polymorpha in Continental USA. Acta Ecologica Sinica 2008, 28(9):4253–4258. 10.1016/S1872-2032(08)60080-3View ArticleGoogle Scholar
- Pineda F: Generalization of backpropagation to recurrent neural networks. Phys Rev Lett 1987, 19(59):2229–2232.View ArticleGoogle Scholar
- Cybenko G: Approximation by superposition of sigmoidal functions. Math Control Syst 1989, 2: 303–314. 10.1007/BF02551274View ArticleGoogle Scholar
- Rumelhart DE, Hinton GE, Williams RJ: Learning internal representations by error propagation. In Parallel Distributed Processing. Edited by: Rumelhart DE, McClelland JL. MIT Press; 1986:318–362.Google Scholar
- Principe JC, Euliano NR, Lefebvre WC: Neural and Adaptive Systems: Fundamentals through Simulations. John Wiley & Sons; 1999.Google Scholar
- Thomann RV, Mueller JA: Principles of Surface Water Quality Modeling and Control. Harper and Row, New York; 1987.Google Scholar
- Smith AE, Mason AK: Cost estimation predictive modeling: Regression versus neural network. The Engineering Economist 1997, 42(2):137–161. 10.1080/00137919708903174View ArticleGoogle Scholar
- Shen Q, Chouchoulas A: FuREAP: a fuzzy-rough estimator of algae populations. Artif Intelligence Eng 2001, 15: 13–24. 10.1016/S0954-1810(00)00022-4View ArticleGoogle Scholar
- Kohonen T: Self organised formation of topological correct feature maps. Biol Cybernetics 1982, 43: 59–69. 10.1007/BF00337288View ArticleGoogle Scholar
- Ross TJ: Fuzzy Logic with Engineering Applications. McGraw-Hill, New York; 1994.Google Scholar
- MacQueen J: Some methods for classification and analysis of multivariable observation. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. Volume 1. Berkeley University of California Press; 1967:281–297.Google Scholar
- Chiu SL: Fuzzy Model Identification Based on Cluster Estimation. J Intel Fuzzy Syst 1994, 2: 267–278.View ArticleGoogle Scholar
- Price KP, Pyke DA, Mendes L: Shrub dieback in a semiarid ecosystem: the integration of remote sensing and geographic informatics systems for detecting vegetation change. Photogrammeng Rem Eng Rem S 1992, 58: 455–463.Google Scholar
- Unni KS, Pawar S: The phytoplankton along a pollution gradient in the river Mahanadi (M.P. state) India – a multivariate approach. Hydrobiologia 2000, 430: 87–96. 10.1023/A:1004025231206View ArticleGoogle Scholar
- Iriarte A, Purdie DA: Factors controlling the timing of major spring bloom events in an UK south coast estuary. Estuar. Coast. Shelf Sci 2004, 61: 679–690. 10.1016/j.ecss.2004.08.002View ArticleGoogle Scholar
- Shapiro J: Current beliefs regarding dominance by bluegreens: the case for the importance of CO2 and pH. Verh int Ver Limnol 1990, 24: 38–54.Google Scholar
- Reynolds CS, Wiseman SW, Clarke MJO: Growth- and loss-rate responses of phytoplankton to intermittent artificial mixing and their application to the control of planktonic algal biomass. J Appl Ecol 1984, 21: 11–39. 10.2307/2403035View ArticleGoogle Scholar
- Campbell KL, Edwards DR: Phosphorus and water quality impacts. In Agricultural Nonpoint Source Pollution:Watershed Management and Hydrology. Edited by: Ritter WF Shirmohammadi A. Lewis Publishers, Boca Raton, F; 2001:91–108.Google Scholar
- Moss B: The influence of environmental factors on the distribution of freshwater algae: an experimental study. I. Introduction and influence of calcium concentration. J Ecol 1973, 61: 157–177. 10.2307/2258925View ArticleGoogle Scholar
- O’Brien WJ, Hershey AE, Hobbie JE, Hullar MA, Kipphut GW, Miller MC, Moller B, Vestal JR: Control mechanisms of arctic lake ecosystems: a limnocorral experiment. Hydrobiol 1992, 240: 143–188. 10.1007/BF00013459View ArticleGoogle Scholar
- Kammat ND: Topics in Algae. Edited by: P.G. Atre. Print Aids, Aurangabad 1; 1982:250.Google Scholar
- Jrgensen SE: Fundamentals of Ecological Modelling. 2nd edition. Elsevier, Amsterdam; 1994:520–527.Google Scholar
- Salski A: Fuzzy knowledge-based models in ecological research. Ecol Model 1992, 63: 103–112. 10.1016/0304-3800(92)90064-LView ArticleGoogle Scholar
- Wehr JD, Descy JP: Use of phytoplankton in large river management. J Phycol 1998, 34: 741–749. 10.1046/j.1529-8817.1998.340741.xView ArticleGoogle Scholar

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