# “The Big One” Earthquake Preparedness Assessment among Younger Filipinos Using a Random Forest Classifier and an Artificial Neural Network

^{1}

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## Abstract

**:**

## 1. Introduction

- Will the random forest classifier surpass the accuracy rate of the basic decision tree as claimed by related studies in line with earthquake preparedness?
- Will the results of the different MLAs be similar for factors affecting earthquake preparedness?
- Can nonlinear relationship frameworks be effectively assessed by MLAs?
- Are the results different from SEMs and MLAs?
- How can the results be practically applied by the Philippines for disaster preparedness?

## 2. Theoretical Framework

## 3. Methodology

#### 3.1. Data Collection

#### 3.2. Data Cleaning and Aggregation

#### 3.3. Prediction Using Machine Learning Algorithms

#### 3.3.1. Decision Tree

#### 3.3.2. Random Forest Classifier

#### 3.3.3. Artificial Neural Network

_{1}and the assigned weights as w

_{1}. The connection of the input layer nodes is parallel to the nodes in the hidden layer. The hidden layer nodes are individually treated, with no weight dependent on another node. Equation (4) presents the calculation of the sum of weights for every hidden node.

_{1}. The considered sum of squared errors between predicted and known values is calculated using Equation (6).

#### 3.3.4. Swish Activation Function (SWAF)

#### 3.3.5. SoftMax Activation Function (SAF)

#### 3.3.6. RMSProp Optimizer

## 4. Results

#### 4.1. Decision Tree

#### 4.2. Random Forest Classifier

#### 4.3. Artificial Neural Network

## 5. Discussion

#### 5.1. Practical Implications

#### 5.2. Contribution and Application

#### 5.3. Limitations and Recommendations

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Parameters | References |
---|---|

Hidden Layer Activation Function | |

Swish | Sharma et al. [45] |

Elu | Feng and Lu [46]; Eckle and Schmidt-Hieber [47] |

Tanh | Sharma et al. [45]; Feng and Lu [46]; Eckle and Schmidt-Hieber [47] |

Output Layer Activation Function | |

SoftMax | Pi et al. [48]; Anbarasan et al. [49]; Satwik and Sundram [50]; Sharma et al. [45] |

ReLu | Jena et al. [51]; Jena and Pradhan [52]; Yousefzadeh et al. [53] |

Sigmoid | Elfwing et al. [54] |

Optimizer | |

Adam | Eckle and Schmidt-Hieber [47] |

RMSProp | Yousefzadeh et al., [53] |

SGD | Jena et al. [51]; Jena and Pradhan [52] |

Category | 40:60 | 50:50 | 60:40 | 70:30 | 80:20 | 90:10 |
---|---|---|---|---|---|---|

Random | ||||||

Gini | 59.21 | 60.01 | 60.89 | 58.77 | 58.77 | 56.80 |

Std. Dev | 2.378 | 3.539 | 2.813 | 2.477 | 2.477 | 4.624 |

Entropy | 57.92 | 60.12 | 60.17 | 60.63 | 58.52 | 56.04 |

Std. Dev | 2.667 | 2.665 | 2.694 | 3.172 | 2.796 | 4.373 |

Best | ||||||

Gini | 57.64 | 60.00 | 60.32 | 63.74 | 60.57 | 56.00 |

Std. Dev | 0.632 | 0.000 | 0.533 | 0.761 | 0.884 | 0.000 |

Entropy | 60.50 | 62.22 | 61.18 | 62.98 | 64.00 | 60.31 |

Std. Dev | 0.997 | 0.646 | 0.531 | 0.379 | 0.000 | 1.525 |

Category | 40:60 | 50:50 | 60:40 | 70:30 | 80:20 | 90:10 |
---|---|---|---|---|---|---|

Random | ||||||

Gini | 90.59 | 89.38 | 89.18 | 89.98 | 88.62 | 86.74 |

Std. Dev | 5.973 | 7.749 | 8.765 | 6.650 | 10.80 | 9.208 |

Entropy | 88.91 | 90.02 | 88.71 | 88.43 | 89.52 | 87.93 |

Std. Dev | 9.770 | 7.624 | 9.502 | 8.958 | 9.687 | 9.542 |

Best | ||||||

Gini | 94.00 | 94.00 | 96.00 | 96.00 | 96.00 | 95.00 |

Std. Dev | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Entropy | 94.00 | 93.00 | 95.00 | 92.00 | 94.00 | 93.00 |

Std. Dev | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

Latent | Activation (H-layer) | Activation (O-layer) | Optimizer | Average Training | STDEV | Average Testing | STDEV |
---|---|---|---|---|---|---|---|

U | Swish | Sigmoid | Adam | 73.982 | 3.687 | 67.361 | 2.508 |

PV | Swish | SoftMax | RMSProp | 75.057 | 2.321 | 68.350 | 1.703 |

PS | Swish | Sigmoid | Adam | 72.091 | 2.978 | 67.386 | 3.109 |

SN | Tanh | SoftMax | Adam | 73.574 | 2.871 | 67.526 | 1.870 |

PBC | Swish | Sigmoid | Adam | 73.787 | 2.557 | 67.385 | 2.501 |

A | Swish | Sigmoid | Adam | 73.209 | 4.083 | 67.422 | 3.598 |

M | Swish | SoftMax | Adam | 72.413 | 3.303 | 68.350 | 2.121 |

U | PV | PS | SN | PBC | A | M | |
---|---|---|---|---|---|---|---|

PV | 0.298 | ||||||

PS | 0.194 | 0.368 | |||||

SN | 0.412 | 0.258 | 0.272 | ||||

PBC | 0.477 | 0.307 | 0.235 | 0.434 | |||

A | 0.144 | 0.297 | 0.364 | 0.425 | 0.182 | ||

M | 0.295 | 0.259 | 0.372 | 0.421 | 0.429 | 0.363 | |

IP | 0.446 | 0.687 | 0.468 | 0.612 | 0.448 | 0.457 | 0.596 |

Ranking | SEM | MLA |
---|---|---|

1 | M | PV |

2 | A | M |

3 | PS | SN |

4 | SN | A |

5 | U | PS |

6 | PV | PBC |

7 | PBC | U |

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## Share and Cite

**MDPI and ACS Style**

Ong, A.K.S.; Zulvia, F.E.; Prasetyo, Y.T.
“The Big One” Earthquake Preparedness Assessment among Younger Filipinos Using a Random Forest Classifier and an Artificial Neural Network. *Sustainability* **2023**, *15*, 679.
https://doi.org/10.3390/su15010679

**AMA Style**

Ong AKS, Zulvia FE, Prasetyo YT.
“The Big One” Earthquake Preparedness Assessment among Younger Filipinos Using a Random Forest Classifier and an Artificial Neural Network. *Sustainability*. 2023; 15(1):679.
https://doi.org/10.3390/su15010679

**Chicago/Turabian Style**

Ong, Ardvin Kester S., Ferani Eva Zulvia, and Yogi Tri Prasetyo.
2023. "“The Big One” Earthquake Preparedness Assessment among Younger Filipinos Using a Random Forest Classifier and an Artificial Neural Network" *Sustainability* 15, no. 1: 679.
https://doi.org/10.3390/su15010679