Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study
Abstract
:1. Introduction
2. Related Works
3. Materials and Methods
3.1. Study Area
3.2. Index Selection
3.3. Data Sources and Monitoring Methods
3.4. Data Preparation
3.5. Long Short-Term Memory Neural Network
4. Results and Discussion
4.1. Rainfall Data
4.2. Monitoring Results of Groundwater
4.3. Modeling Result
4.4. Application of the Concentration Prediction
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. | Indicators | Standards | Measurement Method |
---|---|---|---|
1 | TDS | ”Standard examination methods for drinking water—Organoleptic and physical parameters”(GB/T5750.4-2006) | Gravimetric method |
2 | Fluoride | ”Water Quality-Determination of Fluoride-Ion Selective Elec-trode Method”(GB7484-87) | Ion selective electrode method |
3 | Nitrate | ”Water quality—Determination of nitrate-nitrogen—Ultraviolet spectrophotometry” (HJ/T346-2007) | Ultraviolet spectro- photometry |
4 | Phosphate | ”Standard examination methods for drinking water—Nonmental parameters” (GB/T 5750.5-2006) | Molybdenum blue spectrophotometric method |
5 | Metasilicate | ”Drinking natural mineral water test method” (GB8538-2016) | Molybdosilicate blue photometry |
Methods | TDS | Fluoride | Nitrate | Phosphate | Metasilicate |
---|---|---|---|---|---|
Kolmogorov–Sminov (KS test) | 0.030 | 0.000 | 0.000 | 0.000 | 0.013 |
Shapiro–Wilk (SW test) | 0.000 | 0.000 | 0.000 | 0.000 | 0.039 |
Year | 1# | 2# | 3# | |||
---|---|---|---|---|---|---|
Dry Period | Wet Period | Dry Period | Wet Period | Dry Period | Wet Period | |
2000 | 0.20 | 0.80 | 0.21 | 0.79 | 0.20 | 0.80 |
2001 | 0.24 | 0.96 | 0.20 | 0.80 | 0.24 | 0.76 |
2002 | 0.16 | 0.84 | 0.17 | 0.83 | 0.17 | 0.84 |
2003 | 0.21 | 0.79 | 0.19 | 0.81 | 0.22 | 0.79 |
2004 | 0.15 | 0.84 | 0.15 | 0.85 | 0.15 | 0.84 |
2005 | 0.16 | 0.84 | 0.22 | 0.78 | 0.16 | 0.84 |
2006 | 0.14 | 0.86 | 0.10 | 0.89 | 0.13 | 0.86 |
2007 | 0.05 | 0.95 | 0.07 | 0.93 | 0.05 | 0.95 |
2008 | 0.29 | 0.72 | 0.27 | 0.73 | 0.28 | 0.72 |
2009 | 0.21 | 0.79 | 0.18 | 0.82 | 0.21 | 0.79 |
2010 | 0.08 | 0.92 | 0.11 | 0.89 | 0.07 | 0.92 |
2011 | 0.27 | 0.73 | 0.28 | 0.72 | 0.27 | 0.73 |
2012 | 0.20 | 0.80 | 0.21 | 0.78 | 0.20 | 0.80 |
2013 | 0.25 | 0.75 | 0.30 | 0.70 | 0.25 | 0.75 |
2014 | 0.26 | 0.74 | 0.30 | 0.71 | 0.26 | 0.74 |
Average | 0.19 | 0.82 | 0.20 | 0.80 | 0.19 | 0.81 |
Names of Wells | Geographical Location | Statistical Indicators | TDS (mg/L) | Fluoride (mg/L) | Nitrate (mg/L) | Phosphate (mg/L) | Metasilicate (mg/L) |
---|---|---|---|---|---|---|---|
1 | 121.831 °E /36.985 °N | Avg Std. CV (%) | 421.44 81.02 19.22 | 93.64 51.05 54.52 | 0.17 0.12 73.40 | 0.04 0.03 64.75 | 30.86 5.11 16.55 |
2 | 121.882 °E /37.063 °N | Avg Std. CV (%) | 918.52 351.12 38.23 | 139.60 126.21 90.41 | 0.18 0.11 63.05 | 0.09 0.08 94.91 | 23.75 6.83 28.74 |
3 | 121.886 °E /37.093 °N | Avg Std. CV (%) | 698.09 196.32 28.12 | 100.42 60.81 60.55 | 0.25 0.09 34.51 | 0.30 0.19 62.23 | 21.53 7.37 34.23 |
Names of Wells | Longitute/ Latitude | Statistical Indicators | TDS (mg/L) | Fluoride (mg/L) | Nitrate (mg/L) | Phosphate (mg/L) | Metasilicate (mg/L) |
---|---|---|---|---|---|---|---|
1 | 121.831 °E /36.985 °N | Avg Std. CV (%) | 516.90 195.35 37.79 | 132.22 101.35 76.65 | 0.25 0.11 44.30 | 0.04 0.01 40.54 | 34.84 2.41 6.91 |
2 | 121.882 °E /37.063 °N | Avg Std. CV (%) | 934.70 359.17 38.43 | 186.93 135.35 72.41 | 0.14 0.14 100.56 | 0.18 0.09 47.17 | 26.68 4.29 16.08 |
3 | 121.886 °E /37.093 °N | Avg Std. CV (%) | 764.94 81.64 10.67 | 138.57 38.90 28.07 | 0.28 0.21 77.26 | 0.28 0.14 48.46 | 26.68 4.71 17.67 |
Indicators (mg/L) | MAE (Rainy Period) | MAE (Dry Period) |
---|---|---|
TDS | 89.45 | 76.35 |
Fluoride | 0.09 | 0.06 |
Nitrate | 10.32 | 5.47 |
Phosphate | 0.23 | 0.17 |
Metasilicate | 4.21 | 2.78 |
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Liu, C.; Xu, M.; Liu, Y.; Li, X.; Pang, Z.; Miao, S. Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study. Int. J. Environ. Res. Public Health 2022, 19, 15612. https://doi.org/10.3390/ijerph192315612
Liu C, Xu M, Liu Y, Li X, Pang Z, Miao S. Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study. International Journal of Environmental Research and Public Health. 2022; 19(23):15612. https://doi.org/10.3390/ijerph192315612
Chicago/Turabian StyleLiu, Chao, Mingshuang Xu, Yufeng Liu, Xuefei Li, Zonglin Pang, and Sheng Miao. 2022. "Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study" International Journal of Environmental Research and Public Health 19, no. 23: 15612. https://doi.org/10.3390/ijerph192315612