# Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms

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## Abstract

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## 1. Introduction

## 2. Theoretical Overview

#### 2.1. Multilayer Perceptron Neural Network

#### 2.2. Hybridized MLP–FFA Models

#### 2.3. Hybridized MLP–GSA Model

## 3. Study Area and Data Description

## 4. Model Development and Performance Analysis

## 5. Results and Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Schematic structure of the hybrid multilayer perceptron–firefly algorithm (MLP–FFA) and MLP–gravitational search algorithm (MLP–GSA) methods applied for dew point temperature estimation.

**Figure 5.**Taylor diagram for performance evaluation of the models with respect to Hyderabad station.

Correlation Co-Efficient | Bajpe Station | Hyderabad Station | ||
---|---|---|---|---|

3rd Hour UTC | 12th Hour UTC | 3rd Hour UTC | 12th Hour UTC | |

DPT (${}^{\xb0}$C) | DPT (${}^{\xb0}$C) | DPT (${}^{\xb0}$C) | DPT (${}^{\xb0}$C) | |

WBT (${}^{\xb0}$C) | 0.97 | 0.9 | 0.9 | 0.87 |

RH (%) | 0.75 | 0.8 | 0.69 | 0.83 |

VP (hPa) | 0.99 | 0.99 | 0.98 | 0.99 |

DPT (${}^{\xb0}$C) | 1 | 1 | 1 | 1 |

TRAIN | TEST | ||||||||
---|---|---|---|---|---|---|---|---|---|

WBT (${}^{\xb0}$C) | RH (%) | VP (hPa) | DPT (${}^{\xb0}$C) | WBT (${}^{\xb0}$C) | RH (%) | VP (hPa) | DPT (${}^{\xb0}$C) | ||

Bajpe Station | 3rd Hour UTC | ||||||||

Min | 15.6 | 39 | 11.7 | 9.3 | 22.6 | 73 | 25.3 | 22.1 | |

Max | 24.4 | 78 | 27.9 | 22.9 | 24.8 | 84 | 29.3 | 23.7 | |

Mean | 23.1 | 77 | 25.95 | 21.65 | 24.6 | 82.5 | 29.1 | 23.6 | |

S.Dev. | 1.83 | 1.41 | 2.75 | 1.76 | 0.28 | 2.12 | 0.28 | 0.14 | |

Var. | 3.38 | 2 | 7.6 | 3.12 | 0.08 | 4.5 | 0.08 | 0.02 | |

Skew. | −0.97 | −0.83 | −1.04 | −1.46 | 1.17 | 0.72 | 0.18 | 0.19 | |

12th Hour UTC | |||||||||

Min | 19.8 | 27 | 13.3 | 11.2 | 25 | 65 | 27.6 | 22.7 | |

Max | 24.8 | 66 | 27.1 | 22.4 | 25.8 | 82 | 31.3 | 24.8 | |

Mean | 24.3 | 65 | 26.35 | 21.95 | 25.4 | 73.5 | 29.45 | 23.75 | |

S.Dev | 0.7 | 1.41 | 1.06 | 0.63 | 0.56 | 12.02 | 2.61 | 1.48 | |

Var. | 0.5 | 2 | 1.125 | 0.4 | 0.32 | 144.5 | 6.845 | 2.205 | |

Skew. | −0.72 | 0.1 | −0.86 | −1.27 | −0.57 | −0.14 | −0.18 | −0.31 | |

Hyderabad Station | 3rd Hour UTC | ||||||||

Min | 10.6 | 26 | 7.7 | 3.3 | 17.2 | 29 | 11.3 | 8.8 | |

Max | 19 | 73 | 16.5 | 14.5 | 19 | 86 | 20.8 | 18.1 | |

Mean | 17.7 | 59 | 16.3 | 14.3 | 18.1 | 57.5 | 16.05 | 13.45 | |

S.Dev. | 1.83 | 19.79 | 0.28 | 0.28 | 1.27 | 40.3 | 6.71 | 6.57 | |

Var. | 3.38 | 392 | 0.08 | 0.08 | 1.62 | 162.5 | 45.12 | 43.24 | |

Skew. | −0.57 | −0.35 | −0.34 | −0.75 | −0.93 | −0.3 | −0.69 | −1.14 | |

12th Hour UTC | |||||||||

Min | 17.8 | 25 | 13.9 | 11.9 | 20.4 | 23 | 13.1 | 11 | |

Max | 21.4 | 40 | 14.8 | 12.8 | 20.6 | 55 | 19.6 | 17.2 | |

Mean | 19.6 | 32.5 | 14.35 | 12.35 | 20.5 | 39 | 16.35 | 14.1 | |

S.Dev. | 2.54 | 10.6 | 0.63 | 0.63 | 0.14 | 22.62 | 4.59 | 4.38 | |

Var. | 6.48 | 112.5 | 0.405 | 0.405 | 0.02 | 512 | 21.12 | 19.22 | |

Skew | −0.32 | 0.63 | 0.19 | −0.19 | −0.71 | 0.41 | −0.26 | −0.65 |

MLP–FFA | MLP–GSA |
---|---|

Maximum iterations = 180 | Maximum iterations = 180 |

Population size: 50 | Population size: 50 |

${\beta}_{o}$ = 0.9 | Acceleration Co-efficients ($\alpha ,\beta $) = 1 |

$\gamma $ = 1 | $\omega $ (weighting function) = [0.4, 0.9] |

${\u03f5}_{i}$ = 0.97 | Initial velocities of agents are randomly |

$\alpha $ = 0.6 | generated in the interval [0,1] |

MODEL | Model Parameters/Structure | Testing | |||
---|---|---|---|---|---|

RMSE (${}^{\xb0}$C) | MAE (${}^{\xb0}$C) | NSE | |||

3rd hour UTC | SVM * | 28,8,0.01 | 0.480 | 0.210 | 0.520 |

ELM * | 3-40-1 | 0.380 | 0.040 | 0.690 | |

MLP | (3,16,1) | 0.051 | 0.016 | 0.995 | |

MLP–FFA | (3,16,1) | 0.034 | 0.011 | 0.998 | |

MLP–GSA | (3,16,1) | 0.041 | 0.013 | 0.997 | |

12th hour UTC | SVM * | 28,7,0.01 | 0.520 | 0.28 | 0.62 |

ELM * | 3-90-1 | 0.100 | 0.02 | 0.9 | |

MLP | (3,13,1) | 0.039 | 0.016 | 0.998 | |

MLP–FFA | (3,13,1) | 0.026 | 0.010 | 0.999 | |

MLP–GSA | (3,13,1) | 0.031 | 0.013 | 0.999 | |

Note: Model Parameters of SVM—(C, $\gamma ,\u03f5$); ELM—(input–hidden–output layer neurons); MLP—(input, hidden, output layer neurons) |

MODEL | Model Parameters/Structure | Testing | |||
---|---|---|---|---|---|

RMSE (${}^{\xb0}$C) | MAE (${}^{\xb0}$C) | NSE | |||

3rd hour UTC | SVM * | 37,12,0.01 | 2.360 | 1.040 | 0.630 |

ELM * | 3-50-1 | 0.630 | 0.320 | 0.950 | |

MLP | (3,4,1) | 0.104 | 0.051 | 0.999 | |

MLP–FFA | (3,4,1) | 0.069 | 0.034 | 0.999 | |

MLP–GSA | (3,4,1) | 0.083 | 0.041 | 0.999 | |

12th hour UTC | SVM * | 41,14,0.01 | 1.980 | 1.050 | 0.820 |

ELM * | 3-70-1 | 0.590 | 0.140 | 0.970 | |

MLP | (3,19,1) | 0.134 | 0.052 | 0.999 | |

MLP–FFA | (3,19,1) | 0.089 | 0.034 | 0.999 | |

MLP–GSA | (3,19,1) | 0.107 | 0.041 | 0.999 | |

Note: Model Parameters of SVM—(C, $\gamma ,\u03f5$); ELM—(input–hidden–output layer neurons); MLP—(input, hidden, output layer neurons) |

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## Share and Cite

**MDPI and ACS Style**

Naganna, S.R.; Deka, P.C.; Ghorbani, M.A.; Biazar, S.M.; Al-Ansari, N.; Yaseen, Z.M.
Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms. *Water* **2019**, *11*, 742.
https://doi.org/10.3390/w11040742

**AMA Style**

Naganna SR, Deka PC, Ghorbani MA, Biazar SM, Al-Ansari N, Yaseen ZM.
Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms. *Water*. 2019; 11(4):742.
https://doi.org/10.3390/w11040742

**Chicago/Turabian Style**

Naganna, Sujay Raghavendra, Paresh Chandra Deka, Mohammad Ali Ghorbani, Seyed Mostafa Biazar, Nadhir Al-Ansari, and Zaher Mundher Yaseen.
2019. "Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms" *Water* 11, no. 4: 742.
https://doi.org/10.3390/w11040742