Integration of Geochemical Modeling, Multivariate Analysis, and Irrigation Indices for Assessing Groundwater Quality in the Al-Jawf Basin, Yemen
Abstract
:1. Introduction
2. Materials and Methods
2.1. Site Descriptions and Hydrogeological Settings
2.2. Geological and Hydrogeological Settings
2.3. Water Sampling and Analysis
2.4. Multivariate Statistical Methods and Data Treatments
2.4.1. Cluster Analysis
2.4.2. Principal Component Analysis/Factor Analysis
2.5. Indexing Approach
2.5.1. Index of the Processes Influencing Groundwater Chemistry
2.5.2. Saturation Index (SI)
2.5.3. Irrigation water Quality Indices (IWQIs)
Irrigation Water Quality Index (IWQI)
2.6. Adaptive Neuro-Fuzzy Inference System (ANFIS)
- Model design: In this step, the ANFIS model is designed, including the number of input and output variables, the number of fuzzy rules, and the structure of the adaptive network layer. Based on the complexity of the interactions between the input and output variables, the structure of the ANFIS model can be selected, and the number of fuzzy rules can be determined according to the number of input variables and the desired level of detail in the model;
- Fuzzy partitioning: In this step, the input space is divided into a set of fuzzy regions using fuzzy partitioning techniques such as clustering or grid partitioning. The objective of fuzzy partitioning is to divide the input space into regions such that the input variables in each region are similar with respect to their relationships with the output variable;
- Rule-based generation: In this step, a set of fuzzy rules is generated based on fuzzy partitions and relationships between the input and output variables. Each fuzzy rule consists of an antecedent (the if part of the rule) and a consequent (the then part of the rule), and the antecedent typically consists of a set of fuzzy membership functions that describe the relationship between the input variables and fuzzy regions;
- Model training: In this step, the ANFIS model is trained using a set of training data and an optimization algorithm, such as gradient descent or particle swarm optimization, to adjust the model parameters and improve performance. The objective of the training is to minimize the error between the predicted and observed output values, which can be achieved using a variety of optimization algorithms and loss functions.
2.7. Performance Evaluation of the Simulation Models
- (a)
- Nash–Sutcliffe efficiency coefficient (NSE)
- (b)
- The mean absolute error (MAD)
- (c)
- The absolute variance fraction, R2
- (d)
- The root-mean-square error (RMSE)
2.8. Data Analysis, Processing, and Spatial Distribution
3. Results and Discussion
3.1. Hydrochemical Characteristics of Groundwater
3.2. Groundwater Facies and Processes Influencing Groundwater Chemistry
3.3. Statistical Analysis
3.3.1. Cluster Analysis
3.3.2. Principal Component Analysis (PCA)
3.4. Geochemical Modeling and Mineral Saturation
3.5. Irrigation Water Quality Indices
3.6. Simulation Model (ANFIS)
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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IWQIs | Formula | References |
---|---|---|
IWQI | [31] | |
SAR | [64] | |
Na % | [65] | |
SSP | [66] | |
PS | [67] | |
RSC | [66] |
Qi | SAR | EC (µs/cm) | HCO3− (meq/L) | Na+ (meq/L) | Cl− (meq/L) | HCO3− (meq/L) |
---|---|---|---|---|---|---|
0–35 | SAR > 2 or SAR ≥ 12 | EC < 200 or EC ≥ 3000 | HCO3 < 1 or HCO3 ≥ 8.5 | Na < 2 or SAR ≥ 9 | Cl < 1 or Cl ≥ 10 | HCO3 < 1 or HCO3 ≥ 8.5 |
35–60 | 6 ≤ EC < 12 | 1500 ≤ EC < 3000 | 4.5 ≤ HCO3 < 8.5 | 6 ≤ Na < 9 | 7 ≤ Cl < 10 | 4.5 ≤ HCO3 < 8.5 |
60–85 | 3 ≤ EC < 6 | 750 ≤ EC < 1500 | 1.5 ≤ HCO3 < 4.5 | 3 ≤ Na < 6 | 4 ≤ Cl < 7 | 1.5 ≤ HCO3 < 4.5 |
85–100 | 2 ≤ EC < 3 | 200 ≤ EC < 750 | 1 ≤ HCO3 < 1.5 | 2 ≤ Na < 3 | 1 ≤ Cl < 4 | 1 ≤ HCO3 < 1.5 |
Parameter | Unit | FAO | Min | Max | Average |
---|---|---|---|---|---|
pH | - | 8.5 | 6.5 | 7.5 | 7.13 |
Temp. | (°C) | - | 11.5 | 27.1 | 20.7 |
TDS | (mg/L) | 2000 | 378.42 | 5012 | 1685 |
EC | (μS/cm) | 3000 | 542 | 6628 | 2361.72 |
Ca2+ | (mg/L) | 400 | 40 | 460 | 179.69 |
Mg2+ | (mg/L) | 60 | 32.81 | 328 | 114.5 |
K+ | (mg/L) | 2 | 2.34 | 23.40 | 9.54 |
Na+ | (mg/L) | 919 | 24.15 | 724.5 | 197.2 |
HCO3− | (mg/L) | 610 | 150 | 915 | 408.18 |
Cl− | (mg/L) | 1036 | 35.5 | 1136 | 395 |
SO42− | (mg/L) | 960 | 19.2 | 1939 | 392 |
NO3− | (mg/L) | 10 | 0.1 | 6 | 2.21 |
Parameters | F1 | F2 | F3 |
---|---|---|---|
Ca2+ | 0.770 | 0.450 | 0.133 |
Mg2+ | 0.939 | 0.196 | 0.012 |
Na+ | 0.912 | 0.191 | 0.056 |
K+ | 0.514 | 0.637 | −0.294 |
HCO3− | 0.176 | 0.887 | 0.111 |
Cl− | 0.900 | 0.261 | 0.192 |
SO42− | 0.934 | −0.090 | −0.235 |
CO32− | 0.142 | 0.375 | 0.781 |
TDS | 0.944 | 0.316 | 0.016 |
EC | 0.955 | 0.287 | 0.036 |
NO3− | −0.044 | −0.205 | 0.785 |
Eigenvalue | 6.111 | 1.911 | 1.439 |
Variance % | 55.556 | 17.376 | 13.086 |
Cumulative | 55.556 | 72.932 | 86.018 |
SI | Anhydrite | Aragonite | Calcite | Dolomite | Gypsum | Halite | CO2 (g) |
---|---|---|---|---|---|---|---|
Min. | −2.79 | −0.41 | −0.27 | −0.29 | −2.57 | −7.64 | −2.32 |
Max. | −0.44 | 0.49 | 0.63 | 1.45 | −0.19 | −4.77 | −0.63 |
Mean | −1.501 | 0.003 | 0.151 | 0.404 | −1.269 | −6.075 | −1.649 |
Criteria | Min | Max | Mean | Range | Class | Number of Samples (%) |
---|---|---|---|---|---|---|
IWQI | 17.03 | 96.77 | 61.03 | 85–100 | No restriction | 7 (25.920%) |
70–85 | Low restriction | 4 (14.81%) | ||||
55–70 | Moderate restriction | 4 (14.81%) | ||||
40–55 | High restriction | 7 (25.92%) | ||||
0–40 | Severe restriction | 5 (18.51%) | ||||
SAR | 0.63 | 6.30 | 2.56 | <10 | Excellent | 27 (100%) |
10–18 | Good | 0 (0%) | ||||
19–26 | Fair Poor | 0 (0%) | ||||
>26 | Unsuitable | 0 (0%) | ||||
Na% | 11.35 | 49.73 | 28.29 | <20% | Excellent | 10 (37%) |
21–40% | Good | 13 (48.14%) | ||||
41–60% | Permissible | 4 (14.81%) | ||||
61–80% | Doubtful | 0 (0%) | ||||
>80% | Unsuitable | 50 (100%) | ||||
SSP | 10.39 | 49.45 | 27.57 | <60 | Suitable | 27 (100%) |
>60 | Unsuitable | 0 (0%) | ||||
PS | 1.2 | 52.20 | 15.23 | PS < 3.0 | Excellent to good | 2 (7.40%) |
PS = 3.0–5.0 | Good to injurious | 3 (11.11%) | ||||
PS > 5.0 | Injurious to unsatisfactory | 22 (81.48%) | ||||
RCS | −43.21 | −1.96 | −11.89 | <1.25 | Good | 27 (100%) |
1.25–2.5 | Doubtful | 0 (0%) | ||||
>2.5 | Unsuitable | 0 (0%) |
Index | Performance Criteria | ||||
---|---|---|---|---|---|
R2 | RMSE | MAD | E | ||
Training Series | IWQI | 0.999 | 2.393 | 1.691 | 0.999 |
SAR | 0.973 | 0.098 | 0.067 | 0.973 | |
SSP | 0.996 | 11.202 | 8.802 | 0.996 | |
Anhydrite | 0.970 | 0.003 | 0.002 | 0.971 | |
Aragonite | 0.955 | 0.001 | 0.001 | 0.955 | |
Dolomite | 0.988 | 0.001 | 0.000 | 0.980 | |
Halite | 0.985 | 0.007 | 0.003 | 0.980 | |
Gypsum | 0.976 | 0.002 | 0.001 | 0.979 | |
CO2 | 0.9 80 | 0.002 | 0.001 | 0.985 | |
Testing Series | IWQI | 0.960 | 5.670 | 3.665 | 0.949 |
SAR | 0.940 | 0.479 | 0.222 | 0.868 | |
SSP | 0.892 | 11.667 | 9.880 | 0.864 | |
Anhydrite | 0.895 | 0.354 | 0.159 | 0.775 | |
Aragonite | 0.908 | 0.071 | 0.032 | 0.886 | |
Dolomite | 0.878 | 0.318 | 0.114 | 0.443 | |
Halite | 0.932 | 0.860 | 0.275 | 0.070 | |
Gypsum | 0.964 | 0.174 | 0.056 | 0.958 | |
CO2 | 0.879 | 0.019 | 0.007 | 0.999 |
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Al-Mashreki, M.H.; Eid, M.H.; Saeed, O.; Székács, A.; Szűcs, P.; Gad, M.; Abukhadra, M.R.; AlHammadi, A.A.; Alrakhami, M.S.; Alshabibi, M.A.; et al. Integration of Geochemical Modeling, Multivariate Analysis, and Irrigation Indices for Assessing Groundwater Quality in the Al-Jawf Basin, Yemen. Water 2023, 15, 1496. https://doi.org/10.3390/w15081496
Al-Mashreki MH, Eid MH, Saeed O, Székács A, Szűcs P, Gad M, Abukhadra MR, AlHammadi AA, Alrakhami MS, Alshabibi MA, et al. Integration of Geochemical Modeling, Multivariate Analysis, and Irrigation Indices for Assessing Groundwater Quality in the Al-Jawf Basin, Yemen. Water. 2023; 15(8):1496. https://doi.org/10.3390/w15081496
Chicago/Turabian StyleAl-Mashreki, Mohammed Hezam, Mohamed Hamdy Eid, Omar Saeed, András Székács, Péter Szűcs, Mohamed Gad, Mostafa R. Abukhadra, Ali A. AlHammadi, Mohammed Saleh Alrakhami, Mubarak Ali Alshabibi, and et al. 2023. "Integration of Geochemical Modeling, Multivariate Analysis, and Irrigation Indices for Assessing Groundwater Quality in the Al-Jawf Basin, Yemen" Water 15, no. 8: 1496. https://doi.org/10.3390/w15081496
APA StyleAl-Mashreki, M. H., Eid, M. H., Saeed, O., Székács, A., Szűcs, P., Gad, M., Abukhadra, M. R., AlHammadi, A. A., Alrakhami, M. S., Alshabibi, M. A., Elsayed, S., Khadr, M., Farouk, M., & Ramadan, H. S. (2023). Integration of Geochemical Modeling, Multivariate Analysis, and Irrigation Indices for Assessing Groundwater Quality in the Al-Jawf Basin, Yemen. Water, 15(8), 1496. https://doi.org/10.3390/w15081496