Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Explorative Analysis
2.3. Stepwise Multiple Linear Regression
2.4. Path Analysis
2.5. Machine Learning Methods
- DT is a supervised machine learning technique for inducing a decision tree from training data. A decision tree, also referred to as a classification tree, is a flowchart-like diagram that shows the various outcomes from a series of decisions. Practically, it is the mapping of observations about an item to conclusions about its target value [51].
- k-NN is a relatively simple approach to classification that is completely nonparametric. Given a point x0 that one wishes to classify into one of the K groups, the algorithm finds the k observed data points that are nearest to x0. The classification rule is to assign x0 to the population that has the most observed data points out of the k-nearest neighbors. Points for which there is no majority are either classified to one of the majority populations at random or left unclassified [52].
- SVM is an algorithm that classifies data by determining the optimal hyperplane that separates observations according to their class labels. The central concept of this method is to accommodate classes that are separable by linear and non-linear class boundaries [53].
- RF is a classifier algorithm that evolved from decision trees. It collects the classifications and chooses the most voted prediction as the result. RFs sample data from the original dataset and a subset of features is randomly selected from the optional features to grow the tree at each node. The strength of the RFs relies on the capability to enable a large number of weak or weakly correlated classifiers to form a strong classifier [54].
3. Results and Discussion
3.1. Identifying Variables Explaining CBB Variation
3.2. Describing Dependent Relationships among Variables
3.3. Evaluating the Performance of the Machine Learning Methods
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Country | Number of Lakes | Median/Mean No. of Observations Per Lake | Sampling Month | Time Period |
---|---|---|---|---|
UK | 81 | 3/3.39 | June to October | 2007–2008 |
Denmark | 20 | 19/17.06 | June to September | 1989–2012 |
Norway | 408 | 4/5.41 | May to October | 1988–2009 |
Sweden | 77 | 4.5/6.71 | May to October | 2001–2009 |
Finland | 217 | 1/3.42 | May to October | 1993–2009 |
Lithuania | 19 | 1/2.47 | June to September | 2011–2012 |
Variable | Minimum | Maximum | Median | Mean |
---|---|---|---|---|
Latitude | 50.078 | 69.897 | 59.866 | 60.341 |
Elevation (m a.s.l.) | −4 | 1057 | 126 | 79.9 |
Surface area (km2) | 0.019 | 1377 | 39.1 | 2.1 |
Mean depth (m) | 0.096 | 239 | 12.1 | 6.6 |
Max depth (m) | 1 | 516 | 35.79 | 22 |
Mean air temperature (°C) | −0.3 | 21.1 | 13.6 | 14.1 |
Max air temperature (°C) | 9.8 | 34.1 | 23.58 | 23.8 |
Total nitrogen (μg/L) | 47 | 6841.7 | 656.9 | 435 |
Total phosphorus (μg/L) | 0.5 | 1270 | 28.86 | 12 |
TN/TP | 0.92 | 565 | 34.5 | 43.3 |
Chlorophyll-a (μg/L) | 0 | 310.1 | 10.89 | 4.4 |
Cyanobacteria biomass (mg/L) | 0 | 71.5 | 0.642 | 0.00844 |
Risk Category | Limits According to Cyanobacterial Biomass |
---|---|
Low | CBB ≤ 2 mg/L |
Medium | 2 mg/L< CBB ≤ 10 mg/L |
High | CBB > 10 mg/L |
Linear Model | R2 | BIC | AIC | RMSE |
---|---|---|---|---|
All lakes | ||||
CBB = −0.32 + 27.33 × Chl-a | 0.33 | 20,662 | 20,643 | 2.698 |
CBB = −0.45 + 25.71 × Chl-a + 2.1 × TN | 0.33 | 20,651 | 20,626 | 2.697 |
Shallow lakes | ||||
CBB = 0.03 + 24.99 × Chla | 0.28 | 4704 | 4690 | 5.667 |
CBB = −0.56 + 21.44 × Chl-a + 5.83 × TN | 0.27 | 4698 | 4680 | 5.676 |
CBB = −2.26 + 21.86 × Chl-a + 6.16 × TN + 250.2×MeanDep | 0.27 | 4695 | 4672 | 5.648 |
CBB = −2.64 + 22.26 × Chl-a + 9.63 × TN + 268.13 × MeanDep – 18.13 × TN/TP | 0.27 | 4694 | 4669 | 5.643 |
Deep Lakes | ||||
CBB = −0.4 + 29.48 × Chl-a | 0.43 | 11,695 | 11,676 | 1.294 |
CBB = −0.4 + 26.98 × Chl-a + 35.67 × TN/TP | 0.43 | 11,598 | 11,568 | 1.291 |
CBB = −0.21 + 26.2 × Chl-a – 3.47 × TN + 86.68 × TN/TP | 0.44 | 11,594 | 11,565 | 1.278 |
Scenario | z-Value | Pr (>|z|) |
---|---|---|
1 | ||
Chl-a ~ TN | 21.985 | 0.000 |
Chl-a ~ TP | 24.122 | 0.000 |
Chl-a ~ MeanATemp | 8.346 | 0.000 |
CBB ~ Chl-a | 39.611 | 0.000 |
2 | ||
Chl-a ~ TN, TP, MeanATemp | As in Scenario 1 | |
CBB ~ Chl-a | 32.946 | 0.000 |
CBB ~ TN | 4.389 | 0.000 |
3 | ||
Chl-a ~ TN, TP, MeanATemp | As in Scenario 1 | |
CBB ~ Chl-a | 34.31 | 0.000 |
CBB ~ TP | 0.929 | 0.353 |
4 | ||
Chl-a ~ TN, TP, MeanATemp | As in Scenario 1 | |
CBB ~ Chl-a | 38.327 | 0.000 |
CBB ~ MeanATemp | 1.736 | 0.083 |
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Mellios, N.; Moe, S.J.; Laspidou, C. Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes. Water 2020, 12, 1191. https://doi.org/10.3390/w12041191
Mellios N, Moe SJ, Laspidou C. Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes. Water. 2020; 12(4):1191. https://doi.org/10.3390/w12041191
Chicago/Turabian StyleMellios, Nikolaos, S. Jannicke Moe, and Chrysi Laspidou. 2020. "Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes" Water 12, no. 4: 1191. https://doi.org/10.3390/w12041191
APA StyleMellios, N., Moe, S. J., & Laspidou, C. (2020). Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes. Water, 12(4), 1191. https://doi.org/10.3390/w12041191