# Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow

^{1}

^{2}

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^{*}

## Abstract

**:**

## 1. Introduction

^{2}of 0.817, a correlation coefficient (r) of 0.924, an MAE of 29.034 m

^{3}/s, and an RMSE of 77.218 m

^{3}/s, according to this study.

^{2}). A 4-day-ahead discharge series and the previous week’s average water level produced the best accuracy, with an average RMSE of 0.25 m for five sites across the lake. According to previous research, SVR is a generally applied method in various fields, including predicting power demand [27]. In science [28], water resource management [20,27,28] compared to some ANN approaches [21,29], also needs a small amount of computer memory. However, predicting reservoir inflow with multiple lead times has not been evaluated.

## 2. Materials and Methods

#### 2.1. Research Framework

#### 2.2. Study Area

#### 2.3. Data Used

#### 2.4. Machine Learning Techniques

#### 2.4.1. Multivariable Linear Regression (MLR)

#### 2.4.2. Support Vector Regression (SVR)

#### 2.4.3. Artificial Neural Networks (ANN)

#### 2.4.4. Genetics Algorithm (GA)

#### 2.4.5. Backward Eliminations (BE)

Algorithm 1 Backward Elimination (BE) for Reservoir Inflow Forecasting |

Input: Data set D, Target T. Output: Selected Variables SV |

//D: Huai Nam Sai Reservoir, T: Inflow, SV: R, S, Inf., Climate Indices (10 parameters) |

iterate until SV does not change |

1: while SV changes do |

2://Identify the worst variable Vworst out of all selected variables SV, according to Perf |

3: Vworst ← argmax (V∈SV) Perf(S\V) |

4://Remove Vworst if it does not decrease performance according to criterion C |

5: if Perf(SV\Vworst) ≥ Perf(SV) then |

6: SV ← SV\Vworst |

7: end if |

8: end while |

9: return SV |

#### 2.5. Experimental Setup

#### 2.6. Statistical Performance Measures

## 3. Results and Discussion

#### 3.1. Results of Feature Selection

#### 3.2. Performance Comparison of Prediction Models

#### MLR, ANN, SVR, and Hybrid with BE and GA

^{3}/month. Neither the BE nor the GA was suitable for MLR, SVR, and ANN for twelve-time-ahead. ANN is more suitable for planning annual water management actions than quarterly water management actions. Unlike GA-ANN, which is considered suitable for 6-month water management action planning, BE-ANN is best suited for quarterly and 9-month water management action planning. The Huai Nam Sai Reservoir’s operational plans could benefit from this technique.

## 4. Conclusions

- Feature selection methods (i.e., GA and BE) could improve the performance of SVR and ANN for predicting monthly reservoir inflow forecasting, but they have no effects on MLR. GA and BE could select better features for SVR for all of the lead times. Only BE could make compelling selection features for ANN by improving its performance for almost all of the lead times (i.e., T + 3, T + 6, T + 9) except for twelve lead times (T + 12). GA could overwhelmingly reduce the number of features by more than 60% and 45% for ANN and SVR, respectively. Although BE could improve the ANN and SVR’s performance by approximately 1% over GA, it required a much higher number of features.
- With average an OI and NSE, BE-ANN provides the best performance for 3, 6, and 12 months ahead (T + 3, T + 6, and T + 12). While ANN was suitable for 9 months ahead only. SVR, GA-SVR, and BE-SVR, however, are the least effective of the top three prediction methods.
- Different developed forecasting models were suitable for different reservoir inflow forecasting time-step-ahead. That is, BE-ANN gave the best performance for 3 and 9 months ahead (T + 3 and T + 9), whilst GA-ANN was suitable for semi-annually reservoir inflow forecasting. Finally, ANN provided the best model for annual reservoir inflow forecasting. From the overall results, all SVR-based models (i.e., SVR, GA-SVR, and BE-SVR) gave the lowest performance by giving the lowest values of OI, NSE, and r and the highest values of RMSE and MAE.
- To increase the forecasting models’ performance on reservoir inflow, future studies would have to focus on the extreme events that are frequently happening presently due to climate change effects, i.e., very high peak reservoir inflow, crucially leading to helping reservoir regulators with optimal reservoir operations.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**The basic architecture of the ANN with bias [46].

**Figure 4.**Comparison of nine ML models’ prediction accuracy (i.e., OI, NSE, and r) for four lead times.

**Figure 6.**Scatter plot between observed and simulated reservoir inflow of nine machine learning techniques for four lead times.

Reference | ML Methods | Lead Time | CI | Parameter | Time Interval | ||||
---|---|---|---|---|---|---|---|---|---|

SVR/M | ANN | MLR | Hybrid | Other | |||||

[19] | - | - | - | - | ARMA, ARIMA | - | - | reservoir inflow | monthly |

[22] | SVM | 🗸 | - | GA-SVM | - | T + 1 | - | reservoir inflow | monthly |

[9] | - | - | - | AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF | BE | T + 1, T + 2, …, T + 36 | NINO12, QBO, NTA, AMM 12, NINO4, AMO | reservoir inflow | monthly |

[20] | SVM | 🗸 | - | - | BPN | T + 1, T + 2, …, T + 6 | - | rainfall, reservoir inflow | hourly |

[6] | SVM | 🗸 | 🗸 | - | SMA, BMA | T + 1, T + 2, T + 3 | SOI, ENSO, SST | monthly | |

[21] | SVR | 🗸 | - | RF | T + 1, T + 2 | SOI, Nino1+2, Nino3, Nino34, Nino4, ONI, MEI, PDO, WP, NAO, WHWP, TNI, AO, QBO, CENSO, EPO | inflow | daily | |

[7] | - | 🗸 | - | - | CANFIS, ANFIS | T + 1, T + 2, …, T + 5 | - | inflow | monthly |

[8] | SVR | - | - | - | RF | T + 1, T + 2, …, T + 12 | NINO1+2, ANOM1+2, NINO3, ANOM3, NINO4, ANOM4, NO3.4, ANOM3.4, SOI, DMI | inflow | monthly |

Current Study | SVR | 🗸 | 🗸 | BE-ANN, BE-MLR, BE-SVR, GA-ANN, GA-MLR, GA-SVR | - | T + 3, T + 6, T + 9, T + 12 | NINO1+2, ANOM1+2, NINO3, ANOM3, NINO4, ANOM4, NO3.4, ANOM3.4, SOI, DMI | rainfall, reservoir inflow, reservoir storage | monthly |

Data Used | Features (Monthly) | Types | Data Sources |
---|---|---|---|

Hydrological data | Reservoir inflow (Inf) | Input/Output | The Upper Pak Phanang Operation and Maintenance Project, Irrigation Office 15, Royal Irrigation Department (RID), Thailand |

Rainfall (R) reservoir storage (S) | Input | ||

Ocean indices | Dipole Mode Index (DMI) | Input | The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) |

Southern Oscillation Index (SOI) | Input | ||

Sea surface temperature (SST) | NINO1+2, ANOM1+2, NINO3, ANOM3, NINO4, ANOM4, NO3.4, and ANOM3.4 | Input | The US National Oceanic and Atmospheric Administration (NOAA) |

Data | Statistical Value | |||||
---|---|---|---|---|---|---|

Max | Min | Average | SD | Kurtosis | Skewness | |

NINO1+2 | 27.53 | 18.57 | 22.89 | 2.33 | −1.17 | 0.15 |

ANOM1+2 | 1.64 | −2.10 | −0.24 | 0.80 | −0.57 | 0.15 |

NINO3 | 28.05 | 23.17 | 25.71 | 1.17 | −0.85 | −0.19 |

ANOM3 | 1.53 | −1.81 | −0.17 | 0.70 | −0.38 | −0.05 |

NINO4 | 29.88 | 26.43 | 28.49 | 0.82 | −0.55 | −0.62 |

ANOM4 | 1.25 | −1.71 | −0.07 | 0.75 | −0.84 | −0.40 |

NO3.4 | 28.43 | 24.65 | 26.85 | 0.93 | −0.57 | −0.52 |

ANOM3.4 | 1.72 | −1.92 | −0.18 | 0.79 | −0.33 | −0.06 |

SOI | 4.80 | −5.20 | 0.57 | 1.52 | 0.56 | 0.18 |

DMI | 0.76 | −0.49 | 0.07 | 0.23 | 0.23 | 0.22 |

R | 1017.40 | 0.00 | 172.71 | 164.86 | 7.25 | 2.27 |

Inf | 38.93 | 0.00 | 6.59 | 6.26 | 7.28 | 2.26 |

S | 34.58 | 0.00 | 5.87 | 5.59 | 7.25 | 2.27 |

Methods | Feature Selection Techniques | Symbol |
---|---|---|

Multiple Linear Regression | - | MLR |

Multiple Linear Regression | GA | GA-MLR |

Multiple Linear Regression | BE | BE-MLR |

Support Vector Regression | - | SVR |

Support Vector Regression | GA | GA-SVR |

Support Vector Regression | BE | BE-SVR |

Artificial Neural Networks | ANN | |

Artificial Neural Networks | GA | GA-ANN |

Artificial Neural Networks | BE | BE-ANN |

Methods | No. Selected Features | |||
---|---|---|---|---|

T + 3 | T + 6 | T + 9 | T + 12 | |

ANN | 155 | 155 | 155 | 155 |

GA-ANN | 70 | 67 | 59 | 71 |

BE-ANN | 152 | 154 | 151 | 154 |

MLR | 155 | 155 | 155 | 155 |

GA-MLR | 154 | 154 | 154 | 154 |

BE-MLR | 154 | 154 | 154 | 154 |

SVR | 155 | 155 | 155 | 155 |

GA-SVR | 82 | 90 | 72 | 79 |

BE-SVR | 154 | 154 | 154 | 154 |

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**MDPI and ACS Style**

Weekaew, J.; Ditthakit, P.; Pham, Q.B.; Kittiphattanabawon, N.; Linh, N.T.T.
Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow. *Water* **2022**, *14*, 4029.
https://doi.org/10.3390/w14244029

**AMA Style**

Weekaew J, Ditthakit P, Pham QB, Kittiphattanabawon N, Linh NTT.
Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow. *Water*. 2022; 14(24):4029.
https://doi.org/10.3390/w14244029

**Chicago/Turabian Style**

Weekaew, Jakkarin, Pakorn Ditthakit, Quoc Bao Pham, Nichnan Kittiphattanabawon, and Nguyen Thi Thuy Linh.
2022. "Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow" *Water* 14, no. 24: 4029.
https://doi.org/10.3390/w14244029