Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water
Abstract
:1. Introduction
2. Materials and Methods
2.1. Location of the Study and Data Gathering
2.2. Analysis of Physicochemical Parameters and HM Concentrations
2.3. Neuro-Particle Swarm Optimization Modelling (NN-PSO)
2.3.1. Machine Learning Geostatistical Interpolation (MLGI) Mapping
2.3.2. Data Pre-Processing
2.3.3. Backpropagation Neural Network
2.3.4. Particle Swarm Optimization
2.3.5. Hybrid NN-PSO (hN-PSO)
2.3.6. Performance Validation and Measurement
2.4. Comparison to Other Models
2.5. Sensitivity Analysis
3. Results
3.1. Heavy Metal Concentrations
3.2. Correlation Analysis
3.3. NN-PSO Modelling Results
3.4. Comparison to Other Models
3.5. Sensitivity Analysis Using Olden’s Connection Weight Approach
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Location/Date | Cause of Release | Description of Release | Impact | Reference |
---|---|---|---|---|
Siocon, Zamboanga Del Norte/6 April and 11 July 2007 | Heavy rains eroded the clay soil and destroyed the concrete wall of the zinc extraction sulphide dam. | Contaminated water with detectable levels of cyanide and mercury ran down the Canatuan River and into the Siocon River, eventually reaching the sea. | Reports of siltation had reached up to 3 m thick, which caused frequent flash floods and obstructed irrigation flow from the river. The river mouth became brown and fish catches decreased. | [118] |
Rapu–Rapu Island, Albay/ 11 and 31 October 2005 | The tailings pump failed, spilling tailings from the mill’s emergency pond into the gold processing facility and neighboring Alma and Pagcolbon creeks. | At least 20 cubic meters of slurry material (containing cyanide beyond the standard of 0.05 mg/L and other toxic heavy metals and chemicals) | Two kilograms of dead small fish and crustaceans at the shoreline collected on the same day at the location where the affected creeks exit into the sea. | [119] |
San Marcelino, Zambales 27 August 27 and 11 September 2002 | The spillway of Bayarong tailings dam collapsed during heavy rain. | High concentrations of heavy metals and sulfide materials. | Low-lying settlements were inundated with mining waste, 250 residents were evacuated, and some tailings leaked into Mapanuepe Lake and later into the Sto. Tomas River. | [120] |
Sipalay, Negros Occidental 8 December 1995 | At the Bulawan gold mine, the pressure of impounded tailings created a leakage in the decant tower of tailings pond no. 1. | Mine tailings caused the siltation of the Sipalay River. | Excessive quantities of dust covered a 5-square-kilometer region, affecting the air quality, and local inhabitants reported a rise in respiratory diseases. | [121] |
Toledo City, Cebu 9 August 1999 | The outlet of an open pit’s drainage tunnel (from a closed copper mine) was obstructed, resulting in the loosening of accumulated silt and discharge into the Sapangdaku River toward the sea. | 5.7 million m3 of acidic water | Increased acidity in afflicted water bodies, resulting in fish mortality. | [122] |
Placer, Surigao del Norte/ 26 April 1999 | Tailings pond No. 7 tailings discharged due to a broken concrete pipe. | 700,000 cubic meters of cyanide tailings | Seventeen homes buried, 40 heactares affected, including 20 hectares of agricultural land | [123] |
Sibutad, Zamboanga del Norte 6 November 1997 | Two strong rain events resulted in mudflows and rockslides into a silt dam. | Sibutad gold project’s silt dam overflowed | Caused flash floods damaging the nearby houses and rice fields and fish kills. | [124] |
Mankayan, Benguet 17 October 1986 | Tailings pond 3 collapsed as a result of a compromised dam embankment caused by excessive loading. | Mine tailings overflowed and huge amounts of Cu-contaminated mine wastes carried by the Comillas River | Caused siltation of the Abra River, affecting nine towns, and toxic contamination of the river, depriving the region of about 7.33 million kg of rice every year. | [125] |
Marinduque Island 6 December 1993 | Maguilaguila siltation dam collapsed because of the siltation pressure at the dam wall. | Toxic mine tailings in silt and water | Flooding of the Mogpog River resulted in the death of two children, cattle, contamination of agricultural land, and flooding of downstream communities and Mogpog town. | [126,127] |
Marinduque Island 24 March 1996 | According to the official explanation, the rock around the plug in the Tapian Pit drainage tunnel was cracked, resulting in the plug’s failure. However, in August 1995, the tunnel began to leak. Marcopper/Placer Dome began drilling 160 m down to the tube in September 1995. The drill struck the tube on 24 March 1996, releasing an air pocket that had been holding back tailings and initiating the leak. | The estimate based on the United Nations is between 2–3 million cubic meters over the first 4–5 days of discharge alone. | Approximately 1200 persons were evacuated, 26 km of the Makulapnit and Boac river systems were rendered impassable by tailings, flash floods cut off five communities, and 67,000 cubic meters of bagged tailings were gathered and placed on the banks of the Boac river since the cleaning started in 2000. | [126,127] |
Appendix B
Appendix C
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Abbreviation/ Symbol | Description | Abbreviation/ Symbol | Description |
---|---|---|---|
AAS | Atomic Absorption Spectroscopy | IoT | Internet of Things |
AI | Artificial Intelligence | KGE | Kling-Gupta Efficiency |
AIC | Akaike Information Criterion | KNN | K-Nearest Neighbor |
AMD | Acid Mine Drainage | LM | Levenberg-Marquardt |
ANN | Artificial Neural Network | LSTM | Long Short-Term Memory |
BBO | Biogeography-Based Optimization | LSW | Lake Surface Water |
BP | Back Propagation | M5P | Model Tree |
BR | Bayesian Regularization | MANFIS | Multi-output Adaptive Neuro-Fuzzy Inference System |
CA | Cluster Analysis | MHMI | Modified Heavy Metal Index |
CI | Contamination Index | ML | Machine Learning |
CSW | Coastal Surface Water | MLGI | Machine Learning Geostatistical Interpolation |
EBK | Empirical Bayesian Kriging | MLR | Multiple Linear Regression |
EHCI | Entropy Weight-based HM Conc. Index | NARX | Non-linear AutoRegressive eXogeneous |
FCM | Fuzzy c-means Clustering Method | PLI | Pollution Load Index |
GA | Genetic Algorithm | PMI | Principal Component Analysis-based Metal Index |
GEP | Gene Expression Programming | PSO | Particle Swarm Optimization |
GFF | Generalized Feed Forward | RBF | Radial Basis Function |
GP | Grid Partitioning | RI | Relative Importance |
GRNN | Generalized Regression Neural Network | SCM | Subtractive Clustering Method |
HEI | Heavy Metal Evaluation Index | SPI | Synthetic Pollution Index |
hN-PSO | Hybrid Neuro-Particle Swarm Optimization | SVM | Support Vector Machine |
HPI | Heavy Metal Pollution Index | SVM-Poly | SVM with Polynomial |
ICA | Imperialist Competitive Algorithm | WQG | Water Quality Guidelines |
ICP-OES | Inductively Coupled Plasma-Optical Emission Spectrometry | WQI | Water Quality Index |
Prediction Method | Sample Type | Target Output(s) of the Model | Reference |
---|---|---|---|
ANN–PSO, ANN- Bayesian Regularization (BR) | GW | As, Cu, Pb, Zn | [37] |
ANN–Imperialist Competitive Algorithm (ICA), ANN–Levenberg–Marquardt (LM) | GW | As, Cu, Pb, Zn | [38] |
ANN, ANN–Biogeography–Based Optimization (BBO) Algorithm, Multi-output Adaptive Neuro-Fuzzy Inference System (MANFIS)–Subtractive Clustering Method (SCM) | GW | Fe, Mn, Pb, Zn | [39] |
SVM based Regression–Radial Basis Function (RBF) | GW | Pb, Zn, Cu | [40] |
ANN | GW | Si, Al, Fe, K, Ca, Na, Mg, Cl, Mn, Sr, Br(Groundwater) | [41] |
BP-NN, Nonlinear AutoRegressive eXogenous (NARX) | GW | As | [42] |
ANN | GW | Water Quality Index | [43] |
ANN, MLR | GW | pH, EC, TDS, TH, MHMI, PLI, SPI | [44] |
ANN, Deep Learning | GW | HPI, HEI, CI, EHCI, HMI, PMI | [45] |
MLP-NN, Elman-NN, GFF-NN | GW | Pb, Zn, As | [46] |
BP-NN | GW | Turbidity, Fe, Cl, SO4, TDS, TH, Mn, Zn, KMnO4 Index, NO3-N, NO2-N, NH3-N, F | [47] |
MLR, BP-NN, GEP | SW | WQI | [48] |
NARX, BP-NN | CSW | Cr, Ni, Cu, Pb | [49] |
K-Means CA, BP-NN | LSW | Fe, Cu | [50] |
ANN, SVM | SW | Ti, Cu, Mn, Ni, As, Cd, Sb, Pb | [51] |
MANFIS–Grid Partitioning (GP), MANFIS-SCM, MANFIS–Fuzzy c-means Clustering Method (FCM) | SW | Cu, Fe, Mn, Zn | [52] |
Adaptive Neuro–Fuzzy Inference System (ANFIS) | SW | Cd | [53] |
ANN–LM, ANN–ICA | SW | As, Cu, Pb, Zn | [38] |
ANN | SW | Mn | [42] |
ANN, SVM with Polynomial (SVM-Poly), SVM–RBF, Model Tree (M5P), K–Nearest Neighbor (K-NN) | SW | Cu | [54] |
ANN | SW | Cu | [55] |
SVM, Generalized Regression Neural Network (GRNN) | SW | Cu, Fe, Mn, Zn | [56] |
SVM, ANN | SW | Ni, Fe | [57] |
BP-LM | SW | Cd, Cr, Cu, | [58] |
Parameter | N | Min | Max | Mean | Guidelines | |
---|---|---|---|---|---|---|
Philippine WQG [100] | WHO | |||||
Temp (°C) | 80 | 26.0 | 36.4 | 30.58 | 25–31 | - |
pH | 80 | 2.9 | 9.4 | 6.28 | 6.5–9.0 | 6.5–9.2 |
EC (µS/cm) | 80 | 130.0 | 6000.0 | 2617.21 | - | 1500 |
TDS (mg/L) | 80 | 60.0 | 3000.0 | 1377.66 | - | 1200 |
Cr (mg/L) | 80 | 0.00029 | 0.03766 | 0.01820 | 0.010 | 0.050 |
Cd (mg/L) | 80 | 0.00706 | 0.06122 | 0.04315 | 0.005 | 0.003 |
Fe (mg/L) | 80 | 0.45237 | 2.76195 | 2.32390 | 1.500 | 0.300 |
Mn (mg/L) | 80 | 0.00049 | 11.09783 | 2.07269 | 0.200 | 0.400 |
Zn (mg/L) | 80 | 0.00047 | 9.58050 | 1.69057 | 2.000 | 3.000 |
Ni (mg/L) | 80 | 0.00413 | 0.12689 | 0.10156 | 0.200 | 0.070 |
Pb (mg/L) | 80 | 0.00339 | 0.05608 | 0.03851 | 0.050 | 0.010 |
Cu (mg/L) | 80 | 0.02763 | 17.16567 | 7.67426 | - | 2.000 |
Parameter | N | Min | Max | Mean | Guidelines | |
---|---|---|---|---|---|---|
Philippine WQG [100] | WHO | |||||
Temp (°C) | 80 | 26.7 | 33.7 | 30.26 | 25–31 | - |
pH | 80 | 3.1 | 8.4 | 5.94 | 6.5–9.0 | 6.5–9.2 |
EC (µS/cm) | 80 | 90 | 5380.0 | 2211.00 | - | 1500.00 |
TDS (mg/L) | 80 | 40 | 2670.0 | 1142.35 | - | 1200.00 |
Cr (mg/L) | 80 | 0.00023 | 0.03766 | 0.02937 | 0.010 | 0.05 |
Cd (mg/L) | 80 | 0.00040 | 0.06122 | 0.04459 | 0.005 | 0.003 |
Fe (mg/L) | 80 | 0.06915 | 53.01624 | 21.74808 | 1.5 | 0.3 |
Mn (mg/L) | 80 | 0.00361 | 0.01769 | 0.01027 | 0.2 | 0.4 |
Zn (mg/L) | 80 | 0.02480 | 0.07430 | 0.03922 | 2.00 | 3.00 |
Ni (mg/L) | 80 | 0.00415 | 0.12689 | 0.08820 | 0.20 | 0.07 |
Pb (mg/L) | 80 | 0.00680 | 0.05607 | 0.03458 | 0.05 | 0.01 |
Cu (mg/L) | 80 | 0.00690 | 0.20730 | 0.09144 | - | 2.00 |
Parameter | N | Min | Max | Mean | Guidelines | |
---|---|---|---|---|---|---|
PNSDW 2017 [67] | WHO | |||||
Temp (°C) | 80 | 26.3 | 49.6 | 37.72 | - | - |
pH | 80 | 6.1 | 7.9 | 7.01 | 6.5–8.5 | 6.5–9.2 |
EC (µS/cm) | 80 | 80.0 | 2350.0 | 1140.45 | - | 1500.000 |
TDS (mg/L) | 80 | 30.0 | 1150.0 | 499.12 | 600.000 | 1200.000 |
Cr (mg/L) | 80 | 0.01733 | 0.17182 | 0.07527 | 0.050 | 0.050 |
Cd (mg/L) | 80 | 0.00055 | 0.10389 | 0.06879 | 0.003 | 0.003 |
Fe (mg/L) | 80 | 0.00038 | 54.68567 | 11.50116 | 1.000 | 0.300 |
Mn (mg/L) | 80 | 0.00009 | 8.71857 | 2.44137 | 0.400 | 0.400 |
Zn (mg/L) | 80 | 0.00098 | 56.96133 | 13.95211 | 5.000 | 3.000 |
Ni (mg/L) | 80 | 0.00013 | 0.12530 | 0.08955 | 0.070 | 0.070 |
Pb (mg/L) | 80 | 0.01560 | 0.12178 | 0.10676 | 0.010 | 0.010 |
Cu (mg/L) | 80 | 0.03711 | 0.26050 | 0.21542 | 1.000 | 2.000 |
Parameter | N | Min | Max | Mean | Guidelines | |
---|---|---|---|---|---|---|
PNSDW 2017 [67] | WHO | |||||
Temp (°C) | 80 | 26.2 | 36.7 | 30.25 | - | - |
pH | 80 | 5.6 | 7.9 | 6.85 | 6.5–8.5 | 6.5–9.2 |
EC (µS/cm) | 80 | 20.0 | 2840.0 | 1185.05 | - | 1500.000 |
TDS (mg/L) | 80 | 10.0 | 1400.0 | 601.20 | 600.00 | 1200.000 |
Cr (mg/L) | 80 | 0.01638 | 0.17179 | 0.14767 | 0.050 | 0.050 |
Cd (mg/L) | 80 | 0.00055 | 0.10389 | 0.04458 | 0.003 | 0.003 |
Fe (mg/L) | 80 | 0.16390 | 13.58610 | 9.82432 | 1.000 | 0.300 |
Mn (mg/L) | 80 | 0.00405 | 0.14579 | 0.04089 | 0.400 | 0.400 |
Zn (mg/L) | 80 | 0.02480 | 0.51992 | 0.26563 | 5.000 | 3.000 |
Ni (mg/L) | 80 | 0.00101 | 0.12490 | 0.10005 | 0.070 | 0.070 |
Pb (mg/L) | 80 | 0.05496 | 0.12178 | 0.11831 | 0.010 | 0.010 |
Cu (mg/L) | 80 | 0.00690 | 0.02759 | 0.02257 | 1.000 | 2.000 |
Hidden Neurons | No. of Particles | No. of Iterations | Elapsed Time (sec) | MSE | R | ||
---|---|---|---|---|---|---|---|
Validation | Testing | ||||||
Cr | 30 | 7 | 2000 | 121.2884 | 0.000009 | 0.96878 | 0.98999 |
Cd | 28 | 3 | 2000 | 153.1887 | 0.000021 | 0.95566 | 0.96404 |
Fe | 29 | 9 | 2000 | 150.5006 | 0.004871 | 0.99585 | 0.97976 |
Mn | 27 | 3 | 2000 | 151.0693 | 0.004364 | 0.99933 | 0.98620 |
Zn | 30 | 6 | 2000 | 116.5287 | 0.031773 | 0.99904 | 0.96388 |
Ni | 29 | 6 | 2000 | 115.7815 | 0.000047 | 0.98316 | 0.97981 |
Pb | 30 | 4 | 2000 | 152.8467 | 0.000020 | 0.97832 | 0.96557 |
Cu | 30 | 2 | 2000 | 153.2260 | 0.039010 | 0.99972 | 0.98390 |
Hidden Neurons | No. of Particles | No. of Iterations | Elapsed Time (sec) | MSE | R | ||
---|---|---|---|---|---|---|---|
Validation | Testing | ||||||
Cr | 29 | 2 | 2000 | 157.2246 | 0.0000009 | 0.99050 | 0.98830 |
Cd | 30 | 8 | 2000 | 152.5088 | 0.0000150 | 0.98307 | 0.96868 |
Fe | 28 | 10 | 2000 | 151.0597 | 0.0702760 | 0.98099 | 0.96368 |
Mn | 29 | 10 | 2000 | 138.3126 | 0.0000006 | 0.95686 | 0.96337 |
Zn | 30 | 3 | 2000 | 114.7199 | 0.000005 | 0.98559 | 0.98614 |
Ni | 27 | 1 | 2000 | 120.5753 | 0.000058 | 0.98779 | 0.96227 |
Pb | 29 | 1 | 2000 | 119.6443 | 0.000008 | 0.98377 | 0.98897 |
Cu | 28 | 9 | 2000 | 153.6068 | 0.000269 | 0.96707 | 0.95589 |
Hidden Neurons | No. of Particles | No. of Iterations | Elapsed Time (sec) | MSE | R | ||
---|---|---|---|---|---|---|---|
Validation | Testing | ||||||
Cr | 30 | 8 | 2000 | 154.5653 | 0.000014 | 0.98851 | 0.98640 |
Cd | 28 | 1 | 2000 | 156.7819 | 0.000078 | 0.98910 | 0.98683 |
Fe | 29 | 9 | 2000 | 150.3615 | 0.031866 | 0.98414 | 0.96054 |
Mn | 30 | 6 | 2000 | 158.4757 | 0.040315 | 0.98414 | 0.96002 |
Zn | 29 | 4 | 2000 | 122.3900 | 0.008780 | 0.99965 | 0.99577 |
Ni | 27 | 10 | 2000 | 155.5062 | 0.000073 | 0.97786 | 0.95538 |
Pb | 28 | 9 | 2000 | 124.1062 | 0.000003 | 0.99641 | 0.99788 |
Cu | 29 | 3 | 2000 | 122.3725 | 0.000030 | 0.99663 | 0.99835 |
Hidden Neurons | No. of Particles | No. of Iterations | Elapsed Time (sec) | MSE | R | ||
---|---|---|---|---|---|---|---|
Validation | Testing | ||||||
Cr | 29 | 2 | 2000 | 162.4754 | 0.00014800 | 0.96813 | 0.98011 |
Cd | 27 | 4 | 2000 | 157.0324 | 0.00003800 | 0.99426 | 0.98938 |
Fe | 29 | 8 | 2000 | 145.3954 | 0.04537300 | 0.96040 | 0.97544 |
Mn | 30 | 8 | 2000 | 164.0052 | 0.00007800 | 0.98231 | 0.97926 |
Zn | 28 | 5 | 2000 | 161.1227 | 0.00012300 | 0.99861 | 0.99775 |
Ni | 29 | 7 | 2000 | 160.0693 | 0.00002200 | 0.97463 | 0.98991 |
Pb | 28 | 10 | 2000 | 122.7119 | 0.00000600 | 0.98788 | 0.99495 |
Cu | 30 | 5 | 2000 | 157.5830 | 0.00000005 | 0.99925 | 0.99815 |
Model | Governing Network Structure | Model | Governing Network Structure |
---|---|---|---|
SW Dry Cr | 4-30-1 | GW Dry Cr | 4-30-1 |
SW Dry Cd | 4-28-1 | GW Dry Cd | 4-28-1 |
SW Dry Fe | 4-29-1 | GW Dry Fe | 4-29-1 |
SW Dry Mn | 4-27-1 | GW Dry Mn | 4-30-1 |
SW Dry Zn | 4-30-1 | GW Dry Zn | 4-29-1 |
SW Dry Ni | 4-29-1 | GW Dry Ni | 4-27-1 |
SW Dry Pb | 4-30-1 | GW Dry Pb | 4-28-1 |
SW Dry Cu | 4-30-1 | GW Dry Cu | 4-29-1 |
SW Wet Cr | 4-29-1 | GW Wet Cr | 4-29-1 |
SW Wet Cd | 4-30-1 | GW Wet Cd | 4-27-1 |
SW Wet Fe | 4-28-1 | GW Wet Fe | 4-29-1 |
SW Wet Mn | 4-29-1 | GW Wet Mn | 4-30-1 |
SW Wet Zn | 4-30-1 | GW Wet Zn | 4-28-1 |
SW Wet Ni | 4-27-1 | GW Wet Ni | 4-29-1 |
SW Wet Pb | 4-29-1 | GW Wet Pb | 4-28-1 |
SW Wet Cu | 4-28-1 | GW Wet Cu | 4-30-1 |
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De Jesus, K.L.M.; Senoro, D.B.; Dela Cruz, J.C.; Chan, E.B. Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water. Toxics 2022, 10, 95. https://doi.org/10.3390/toxics10020095
De Jesus KLM, Senoro DB, Dela Cruz JC, Chan EB. Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water. Toxics. 2022; 10(2):95. https://doi.org/10.3390/toxics10020095
Chicago/Turabian StyleDe Jesus, Kevin Lawrence M., Delia B. Senoro, Jennifer C. Dela Cruz, and Eduardo B. Chan. 2022. "Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water" Toxics 10, no. 2: 95. https://doi.org/10.3390/toxics10020095