Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Study Area
2.2. Hydrogeology of Study Area and Data Acquisition
- Aquifer type group I is composed of different types of basalt rocks, like weathered, dense, and vesicular. Groundwater occurs under water table conditions 30 m or lower than ground level. The tube well discharge varies from 97 to 227 m3/h for drawdown between 2.68 m and 0.68 m.
- Aquifer type group II is mainly sandstone, siltstone, limestone, and schist. The piezometric head of the horizontal flowing wells lies between 6.63 and 8.92 m above the groundwater level. In the non-flowing wells, it ranges between 1.55 and 11.34 m below the ground level. Most tube wells constructed in this region register a free flow, ranging from 80 to 210 m3/h. In the non-flowing wells, the discharge head varies from 2 to 8 m.
- Aquifer type group III is composed of gravel, pebbles, grit, sand, clay, etc. Quaternary aquifers belong to this group. Groundwater occurs under unconfined conditions in surface or near-surface aquifers. Water depth varies from 0.2 to 9.7 m.
2.3. Genetic Algorithm (GA)
- Start: Generate chromosomes by random population.
- Fitness: Determine the fitness function in the populations of every chromosome.
- New Population: Develop the new population by following the steps that follow until completing the new population.
- (a)
- Selection: Based on their fitness, identify two parent chromosomes from a population.
- (b)
- Crossover: Cross the parents to create a new spring (children), with the possibility of a crossover. When there is no crossover, offspring are the exact duplicate of the parents.
- (c)
- Mutation: Mutate new offspring at each locus, with the likelihood of mutation.
- (d)
- Accepting: In the new population, locate new offspring.
- Replace: For the further running of the algorithm, use the newly created population.
- Test: When the ended conditions are encountered, the current population’s best outcome will stop and return.
- Loop: Switch back to Step 2.
2.4. Hybrid Genetic Algorithm-Artificial Neural Network (GA-ANN)
- The GA technique is used to improve the topology of the ANN and its variables.
- The optimal response is obtained using ANN.
2.5. Determination of the Parameters the ANN Model
2.6. Development of GA-ANN and GA Models for GWTD Prediction
2.7. Statistical Indicators
3. Results and Discussion
3.1. Prediction of GWTD Using Traditional GA Method
3.2. Prediction of GWTD Using GA-ANN Models
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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GA Parameter | Type/Value |
---|---|
Population size | 50 |
Population type | Double vector |
Population initial range | [3 × 1double] |
Selection Mechanism | Roulette wheel |
Basis of chromosome selection | Fitness function (MSE) |
Crossover type | Double |
Crossover Probability | 0.8–1.0 |
Mutation type | Gaussian |
Mutation Probability | 0.001–0.01 |
Elite count | 2 |
Migration direction | Forward |
Migration interval | 20 |
Time limit | Infinite |
Stall Generation limit | Inf |
Maximum number of generations | 100 |
Termination Criteria | 0.001 m2 |
Display | Iteration |
Model | Output | Input |
---|---|---|
Pre-monsoon season | ||
1 | WTpr | F[(ms,n−1) + (Nms,n−1 to n), (ms,n−1) + (Nms,n−1 to n), WTpr,n−1] |
2 | WTpr | F[(ms,n−1) + (Nms,n−1 to n), (ms,n−1) + (Nms,n−1 to n), WTpr,n−1, WTps,n−1] |
3 | WTpr | F[(ms,n−1) + (Nms,n−1 to n), (ms,n−1) + (Nms,n−1 to n), ∆WT(pr,n−1–ps,n−1) ] |
4 | WTpr | F[(Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2),(Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2), WTpr,n−2] |
5 | WTpr | F[(Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2), (Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2), WTpr,n−2, WTps,n−2, WTpr,n−1, WTps,n−1] |
6 | WTpr | F[(Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2), (Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2), ∆WT(pr,n−2–ps,n−2), ∆WT(ps,n−2–pr,n−1), ∆WT(pr,n−1–ps,n−1)] |
7 | WTpr | F[(Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2) + (Nms,n−3 to n−2) + (ms,n−3), (Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2) + (Nms,n−3 to n−2) + (ms,n−3), WTpr,n−3] |
8 | WTpr | F[(Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2) + (Nms,n−3 to n−2) + (ms,n−3), (Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2) + (Nms,n−3 to n−2) + (ms,n−3), WTpr,n−3, WTps,n−3, WTpr,n−2, WTps,n−2, WTpr,n−1, WTps,n−1] |
9 | WTpr | F[(Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2) + (Nms,n−3 to n−2) + (ms,n−3),(Nms,n−1 to n) + (ms,n−1) + (Nms,n−2 to n−1) + (ms,n−2) + (Nms,n−3 to n−2) + (ms,n−3), ∆WT(pr,n−3–ps,n−3), ∆WT(ps,n−3–pr,n−2), ∆WT(pr,n−2–ps,n−2), ∆WT(ps,n−2–pr,n−1), ∆WT(pr,n−1–ps,n−1)] |
Post-monsoon season | ||
1 | WTps | F[(ms,n) + (Nms,n−1 to n), (ms,n) + (Nms,n−1 to n), WTps,n−1] |
2 | WTps | F[(ms,n) + (Nms,n−1 to n), (ms,n) + (Nms,n−1 to n), WTps,n−1, WTpr,n] |
3 | WTps | F[(ms,n) + (Nms,n−1 to n), (ms,n) + (Nms,n−1 to n), ∆WT(ps,n−1–pr,n)] |
4 | WTps | F[(ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2), (ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2), WTps,n−2] |
5 | WTps | F[(ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2), (ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2), WTps,n−2, WTpr,n−1, WTps,n−1, WTpr,n] |
6 | WTps | F[(ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2), (ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2), ∆WT(ps,n−2–pr,n−1), ∆WT(pr,n−1–ps,n−1), ∆WT(ps,n−1–pr,n)] |
7 | WTps | F[(ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2) + (ms,n−2) + (Nms,n−3 to n−2),(ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2) + (ms,n−2) + (Nms,n−3 to n−2), WTps,n−3] |
8 | WTps | F[(ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2) + (ms,n−2) + (Nms,n−3 to n−2), (ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2) + (ms,n−2) + (Nms,n−3 to n−2), WTps,n−3, WTpr,n−2, WTps,n−2, WTpr,n−1, WTps,n−1, WTpr,n] |
9 | WTps | F[(ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2) + (ms,n−2) + (Nms,n−3 to n−2),(ms,n) + (Nms,n−1 to n) + (ms,n−1) + (Nms,n−1 to n−2) + (ms,n−2) + (Nms,n−3 to n−2), ∆WT(ps,n−3–pr,n−2), ∆WT(pr,n−2–ps,n−2), ∆WT(ps,n−2–pr,n−1), ∆WT(pr,n−1–ps,n−1), ∆WT(ps,n−1–pr,n)] |
Model | Population Size | Generation Limit | Minimum RMSE | Generation at Minimum RMSE |
---|---|---|---|---|
Pre-monsoon | ||||
GA-1 | 50 | 60 | 3.95 | 45 |
GA-2 | 50 | 60 | 2.42 | 43 |
GA-3 | 100 | 120 | 3.18 | 85 |
GA-4 | 150 | 200 | 4.44 | 120 |
GA-5 | 150 | 200 | 2.26 | 120 |
GA-6 | 150 | 200 | 4.57 | 120 |
GA-7 | 100 | 150 | 4.65 | 120 |
GA-8 | 150 | 200 | 4.70 | 120 |
GA-9 | 150 | 200 | 4.01 | 135 |
Post-monsoon | ||||
GA-1 | 100 | 120 | 4.41 | 65 |
GA-2 | 100 | 120 | 4.54 | 100 |
GA-3 | 150 | 200 | 3.45 | 100 |
GA-4 | 100 | 150 | 4.03 | 86 |
GA-5 | 100 | 150 | 5.89 | 84 |
GA-6 | 100 | 150 | 5.49 | 95 |
GA-7 | 150 | 200 | 5.56 | 135 |
GA-8 | 150 | 200 | 2.73 | 150 |
GA-9 | 150 | 200 | 5.31 | 150 |
Model | Period | R2 | CE | r | MAD | MSE | CVRE | APE | PI |
---|---|---|---|---|---|---|---|---|---|
GA-1 | Training | 0.26 | −0.76 | 0.03 | 1.01 | 19.60 | 0.14 | 0.14 | 1.45 |
Testing | 0.39 | −0.31 | 0.62 | 2.63 | 15.60 | 0.65 | 0.33 | 0.05 | |
GA-2 | Training | 0.40 | −6.19 | 0.55 | 5.02 | 11.23 | 0.12 | 0.72 | 1.28 |
Testing | 0.39 | 0.17 | 0.62 | 2.68 | 5.86 | 0.48 | 0.29 | 0.03 | |
GA-3 | Training | 0.11 | −1.16 | 0.39 | 6.01 | 25.77 | 0.91 | 0.08 | 0.03 |
Testing | 0.02 | −0.14 | 0.14 | 3.9 | 10.11 | 0.56 | 0.42 | 0.04 | |
GA-4 | Training | 0.40 | 0.35 | 0.03 | 0.52 | 9.33 | 0.75 | 0.07 | 0.02 |
Testing | 0.39 | 0.15 | 0.62 | 2.67 | 19.71 | 0.48 | 0.29 | 0.07 | |
GA-5 | Training | 0.54 | 0.43 | 0.60 | 0.52 | 4.75 | 0.10 | 0.07 | 0.01 |
Testing | 0.42 | 0.33 | 0.65 | 2.14 | 5.11 | 0.43 | 0.23 | 0.03 | |
GA-6 | Training | 0.23 | 0.42 | 0.54 | 0.71 | 7.06 | 0.40 | 0.1 | 0.02 |
Testing | 0.15 | 0.14 | 0.39 | 3.65 | 20.88 | 0.49 | 0.39 | 0.03 | |
GA-7 | Training | 0.47 | 0.32 | 0.50 | 0.71 | 8.3 | 0.43 | 0.13 | 0.02 |
Testing | 0.36 | 0.11 | 0.60 | 2.34 | 21.62 | 0.50 | 0.25 | 0.03 | |
GA-8 | Training | 0.29 | 0.05 | 0.40 | 0.64 | 11.54 | 0.52 | 0.09 | 0.02 |
Testing | 0.34 | 0.08 | 0.58 | 2.57 | 22.1 | 0.51 | 0.28 | 0.04 | |
GA-9 | Training | 0.44 | 0.07 | 0.05 | 1.69 | 11.07 | 0.47 | 0.24 | 0.02 |
Testing | 0.39 | 0.16 | 0.62 | 3.29 | 16.08 | 0.45 | 0.36 | 0.03 |
Model | Period | R2 | CE | r | MAD | MSE | CVRE | APE | PI |
---|---|---|---|---|---|---|---|---|---|
GA-1 | Training | 0.43 | 0.47 | 0.40 | 0.75 | 10.81 | 0.57 | 0.13 | 0.02 |
Testing | 0.40 | 0.19 | 0.63 | 2.56 | 19.45 | 0.51 | 0.29 | 0.03 | |
GA-2 | Training | 0.42 | 0.23 | 0.40 | 0.67 | 11.71 | 0.59 | 0.11 | 0.02 |
Testing | 0.39 | 0.14 | 0.36 | 2.52 | 20.61 | 0.53 | 0.29 | 0.03 | |
GA-3 | Training | 0.13 | −0.29 | 0.08 | 0.33 | 19.78 | 0.78 | 0.04 | 0.03 |
Testing | 0.01 | −0.24 | 0.32 | 4.11 | 11.9 | 0.63 | 0.48 | 0.05 | |
GA-4 | Training | 0.33 | 0.19 | 0.04 | 0.57 | 12.68 | 0.67 | 0.09 | 0.02 |
Testing | 0.39 | 0.07 | 0.36 | 2.38 | 16.24 | 0.55 | 0.27 | 0.03 | |
GA-5 | Training | 0.27 | 0.08 | 0.04 | 0.80 | 13.93 | 0.53 | 0.13 | 0.03 |
Testing | 0.29 | −0.44 | 0.54 | 2.74 | 34.69 | 0.68 | 0.32 | 0.04 | |
GA-6 | Training | 0.08 | −0.30 | 0.2 | 0.77 | 20.44 | 0.85 | 0.13 | 0.03 |
Testing | 0.01 | −0.24 | 0.1 | 4.18 | 30.14 | 0.63 | 0.48 | 0.03 | |
GA-7 | Training | 0.41 | 0.32 | 0.05 | 0.62 | 10.31 | 0.52 | 0.10 | 0.02 |
Testing | 0.39 | −0.31 | 0.36 | 2.82 | 30.91 | 0.65 | 0.33 | 0.04 | |
GA-8 | Training | 0.56 | 0.51 | 0.47 | 0.27 | 7.42 | 0.46 | 0.04 | 0.02 |
Testing | 0.47 | 0.31 | 0.68 | 1.87 | 7.45 | 0.47 | 0.22 | 0.03 | |
GA-9 | Training | 0.06 | −0.32 | 0.06 | 0.9 | 20.12 | 0.77 | 0.15 | 0.03 |
Testing | 0.07 | −0.19 | 0.26 | 3.91 | 28.19 | 0.62 | 0.46 | 0.06 |
Model | Data Set | Population Size = 50 | Population Size = 100 | Population Size = 200 | |||
---|---|---|---|---|---|---|---|
MSE | MSE | MSE | |||||
Pre-Monsoon | Post-Monsoon | Pre-Monsoon | Post-Monsoon | Pre-Monsoon | Post-Monsoon | ||
GA-ANN-1 | Training | 0.20 | 0.20 | 0.50 | 0.20 | 0.50 | 0.30 |
Testing | 0.30 | 0.10 | 0.30 | 0.30 | 0.30 | 0.20 | |
GA-ANN-2 | Training | 0.60 | 0.30 | 0.60 | 0.60 | 0.50 | 0.60 |
Testing | 0.40 | 0.20 | 0.50 | 0.40 | 0.40 | 0.50 | |
GA-ANN-3 | Training | 0.15 | 0.14 | 0.15 | 0.15 | 0.14 | 0.18 |
Testing | 0.12 | 0.12 | 0.13 | 0.14 | 0.13 | 0.16 | |
GA-ANN-4 | Training | 0.60 | 0.30 | 0.60 | 0.30 | 0.80 | 0.20 |
Testing | 0.40 | 0.20 | 0.50 | 0.25 | 0.50 | 0.30 | |
GA-ANN-5 | Training | 0.30 | 0.40 | 0.30 | 0.40 | 0.60 | 0.60 |
Testing | 0.10 | 0.10 | 0.20 | 0.20 | 0.40 | 0.20 | |
GA-ANN-6 | Training | 0.12 | 0.11 | 0.12 | 0.11 | 0.14 | 0.15 |
Testing | 0.10 | 0.90 | 0.11 | 0.12 | 0.12 | 0.13 | |
GA-ANN-7 | Training | 0.40 | 0.40 | 0.50 | 0.40 | 0.50 | 0.60 |
Testing | 0.30 | 0.30 | 0.40 | 0.60 | 0.40 | 0.20 | |
GA-ANN-8 | Training | 0.30 | 0.30 | 0.30 | 0.30 | 0.30 | 0.30 |
Testing | 0.20 | 0.20 | 0.50 | 0.60 | 0.40 | 0.50 | |
GA-ANN-9 | Training | 0.10 | 0.20 | 0.11 | 0.30 | 0.13 | 0.25 |
Testing | 0.06 | 0.10 | 0.09 | 0.20 | 0.11 | 0.10 |
Model | Optimal Population Size | Optimal Generation | MSE | |||
---|---|---|---|---|---|---|
Pre-Monsoon | Post-Monsoon | Pre-Monsoon | Post-Monsoon | Pre-Monsoon | Post-Monsoon | |
GA-ANN-1 | 49 | 49 | 35 | 45 | 0.02 | 0.03 |
GA-ANN-2 | 35 | 37 | 30 | 35 | 0.04 | 0.02 |
GA-ANN-3 | 45 | 50 | 40 | 45 | 0.19 | 0.46 |
GA-ANN-4 | 42 | 42 | 30 | 30 | 0.08 | 0.06 |
GA-ANN-5 | 39 | 47 | 35 | 35 | 0.05 | 0.05 |
GA-ANN-6 | 47 | 48 | 45 | 45 | 0.72 | 0.62 |
GA-ANN-7 | 47 | 32 | 30 | 25 | 0.02 | 0.02 |
GA-ANN-8 | 42 | 47 | 35 | 40 | 0.06 | 0.03 |
GA-ANN-9 | 49 | 48 | 45 | 45 | 0.94 | 0.95 |
Pre-Monsoon | Post-Monsoon | ||||
---|---|---|---|---|---|
Model | Neuron in the Hidden Layer | Mean Square Error | Model | Neuron in the Hidden Layer | Mean Square Error |
GA-ANN-1 | 4 | 0.02 | GA-ANN-1 | 7 | 0.03 |
GA-ANN-2 | 6 | 0.04 | GA-ANN-2 | 7 | 0.02 |
GA-ANN-3 | 5 | 0.19 | GA-ANN-3 | 8 | 0.46 |
GA-ANN-4 | 6 | 0.08 | GA-ANN-4 | 11 | 0.06 |
GA-ANN-5 | 7 | 0.05 | GA-ANN-5 | 8 | 0.05 |
GA-ANN-6 | 8 | 0.72 | GA-ANN-6 | 12 | 0.62 |
GA-ANN-7 | 6 | 0.02 | GA-ANN-7 | 7 | 0.02 |
GA-ANN-8 | 9 | 0.06 | GA-ANN-8 | 9 | 0.03 |
GA-ANN-9 | 8 | 0.94 | GA-ANN-9 | 8 | 0.95 |
Model | Structure | Dataset | R2 | CE | r | MAD | RMSE | CVRE | APE | PI |
---|---|---|---|---|---|---|---|---|---|---|
GA-ANN-1 | 3-4-1 | Training | 0.93 | 0.88 | 0.96 | 0.56 | 0.25 | 0.14 | 0.11 | 0.04 |
Testing | 0.92 | 0.89 | 0.95 | 0.59 | 0.28 | 0.13 | 0.10 | 0.03 | ||
GA-ANN-2 | 4-6-1 | Training | 0.90 | 0.87 | 0.95 | 0.57 | 0.30 | 0.15 | 0.12 | 0.05 |
Testing | 0.89 | 0.84 | 0.94 | 0.65 | 0.59 | 0.16 | 0.13 | 0.04 | ||
GA-ANN-3 | 3-5-1 | Training | 0.89 | 0.82 | 0.94 | 0.85 | 0.85 | 0.19 | 0.14 | 0.06 |
Testing | 0.54 | 0.46 | 0.73 | 0.75 | 0.96 | 0.31 | 0.16 | 0.07 | ||
GA-ANN-4 | 3-6-1 | Training | 0.80 | 0.77 | 0.79 | 0.62 | 0.78 | 0.19 | 0.12 | 0.06 |
Testing | 0.83 | 0.79 | 0.89 | 0.54 | 0.76 | 0.17 | 0.09 | 0.08 | ||
GA-ANN-5 | 6-7-1 | Training | 0.86 | 0.89 | 0.93 | 0.59 | 0.76 | 0.18 | 0.02 | 0.03 |
Testing | 0.85 | 0.82 | 0.92 | 0.49 | 0.74 | 0.14 | 0.04 | 0.05 | ||
GA-ANN-6 | 5-8-1 | Training | 0.83 | 0.82 | 0.92 | 0.65 | 0.79 | 0.21 | 0.10 | 0.06 |
Testing | 0.75 | 0.80 | 0.87 | 0.56 | 0.80 | 0.15 | 0.10 | 0.09 | ||
GA-ANN-7 | 3-6-1 | Training | 0.86 | 0.90 | 0.92 | 0.49 | 0.31 | 0.15 | 0.11 | 0.07 |
Testing | 0.84 | 0.82 | 0.91 | 0.61 | 0.42 | 0.13 | 0.10 | 0.06 | ||
GA-ANN-8 | 8-9-1 | Training | 0.91 | 0.91 | 0.96 | 0.45 | 0.22 | 0.12 | 0.01 | 0.03 |
Testing | 0.94 | 0.94 | 0.97 | 0.48 | 0.17 | 0.11 | 0.03 | 0.02 | ||
GA-ANN-9 | 7-8-1 | Training | 0.75 | 0.59 | 0.87 | 0.84 | 0.75 | 0.21 | 0.15 | 0.08 |
Testing | 0.62 | 0.45 | 0.79 | 0.87 | 0.98 | 0.23 | 0.19 | 0.04 |
Model | Structure | Dataset | R2 | CE | r | MAD | RMSE | CVRE | APE | PI |
---|---|---|---|---|---|---|---|---|---|---|
GA-ANN-1 | 3-7-1 | Training | 0.89 | 0.87 | 0.94 | 0.58 | 0.52 | 0.17 | 0.14 | 0.04 |
Testing | 0.92 | 0.93 | 0.96 | 0.53 | 0.54 | 0.15 | 0.11 | 0.03 | ||
GA-ANN-2 | 4-7-1 | Training | 0.87 | 0.85 | 0.93 | 0.64 | 0.75 | 0.21 | 0.17 | 0.06 |
Testing | 0.90 | 0.87 | 0.95 | 0.59 | 0.56 | 0.18 | 0.12 | 0.04 | ||
GA-ANN-3 | 3-8-1 | Training | 0.88 | 0.86 | 0.94 | 0.60 | 0.58 | 0.19 | 0.16 | 0.05 |
Testing | 0.77 | 0.75 | 0.88 | 0.53 | 0.69 | 0.30 | 0.15 | 0.05 | ||
GA-ANN-4 | 3-6-1 | Training | 0.74 | 0.69 | 0.86 | 0.85 | 0.88 | 0.21 | 0.13 | 0.05 |
Testing | 0.89 | 0.85 | 0.89 | 0.84 | 0.79 | 0.14 | 0.14 | 0.04 | ||
GA-ANN-5 | 6-8-1 | Training | 0.86 | 0.81 | 0.93 | 0.60 | 0.79 | 0.19 | 0.12 | 0.04 |
Testing | 0.92 | 0.93 | 0.96 | 0.53 | 0.55 | 0.12 | 0.11 | 0.02 | ||
GA-ANN-6 | 5-12-1 | Training | 0.60 | 0.62 | 0.77 | 0.72 | 1.15 | 0.56 | 0.15 | 0.08 |
Testing | 0.50 | 0.57 | 0.71 | 0.69 | 1.35 | 0.86 | 0.86 | 0.09 | ||
GA-ANN-7 | 3-7-1 | Training | 0.86 | 0.82 | 0.93 | 0.83 | 0.35 | 0.19 | 0.11 | 0.06 |
Testing | 0.88 | 0.87 | 0.94 | 0.80 | 0.51 | 0.22 | 0.22 | 0.06 | ||
GA-ANN-8 | 8-9-1 | Training | 0.89 | 0.90 | 0.94 | 0.56 | 0.31 | 0.15 | 0.11 | 0.03 |
Testing | 0.95 | 0.96 | 0.97 | 0.45 | 0.42 | 0.13 | 0.10 | 0.01 | ||
GA-ANN-9 | 7-8-1 | Training | 0.77 | 0.74 | 0.88 | 0.89 | 0.79 | 0.25 | 0.16 | 0.07 |
Testing | 0.71 | 0.54 | 0.78 | 0.79 | 1.01 | 0.45 | 0.45 | 0.09 |
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Pandey, K.; Kumar, S.; Malik, A.; Kuriqi, A. Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India. Sustainability 2020, 12, 8932. https://doi.org/10.3390/su12218932
Pandey K, Kumar S, Malik A, Kuriqi A. Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India. Sustainability. 2020; 12(21):8932. https://doi.org/10.3390/su12218932
Chicago/Turabian StylePandey, Kusum, Shiv Kumar, Anurag Malik, and Alban Kuriqi. 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India" Sustainability 12, no. 21: 8932. https://doi.org/10.3390/su12218932