# Application of GIS and Machine Learning to Predict Flood Areas in Nigeria

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. An Overview of Machine Learning and Its Relevance to Flood Prediction

#### 1.2. Flood Prediction and Modelling with ML Models

## 2. Materials and Methods

#### 2.1. Description of the Study Area

^{2}with a total landmass of about 910,770 km

^{2}and water bodies of about 13,000 km

^{2}. Its coastline spans roughly 853 km along the Gulf of Guinea. Nigeria’s topography is divided into five regions: the coast along the Gulf of Guinea; the north-central plateaus; the rivers Niger-Benue, plateaus around the north borders; and mountainous zones to the east, with Chappal Waddi at 2419 m its highest point.

#### 2.2. Methodology

#### 2.2.1. Inventory Map of Historical Flood Events

#### 2.2.2. Flood Conditioning Factors

#### 2.3. Machine Learning Models

#### 2.3.1. Artificial Neural Network (ANN)

#### 2.3.2. Logistic Regression (LR)

#### 2.4. Correlation Analysis

#### 2.5. Pearson’s Correlation Coefficients Estimation

#### 2.6. Variable Importance Estimation

#### 2.7. Assessment of Modeling Accuracy

#### 2.8. Model Performance Evaluation

## 3. Results

#### 3.1. Artificial Neural Network Model (ANN)

#### 3.2. Logistic Regression Model (LR)

#### 3.3. Flood Susceptibility Map

#### 3.4. Validation and Accuracy Assessment

## 4. Discussion

#### 4.1. Variable Importance in Flood Susceptibility

#### 4.2. Analysis of Flood Susceptibility Model Results

#### 4.3. Correlation Analysis Results

#### 4.4. Performance of the ANN and LR Models

#### Classification Performance

#### 4.5. Advantages and Future Study

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## References

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**Figure 4.**The flood conditioning factors: (

**A**) elevation; (

**B**) TWI; (

**C**) SPI; (

**D**) roughness; (

**E**) slope; (

**F**) aspect; (

**G**) curvature; (

**H**) distance to water; (

**I**) distance to road; (

**J**) distance to rail; (

**K**) rainfall; (

**L**) temperature; (

**M**) land cover; (

**N**) soil type; and (

**O**) curve number.

**Figure 5.**The ANN modelling generalised weights (continuous variables) (

**a**) TWI; (

**b**) SPI; (

**c**) roughness; (

**d**) elevation; (

**e**) curvature; (

**f**) slope; (

**g**) distance to water (water) (

**h**) distance to road (road); (

**i**) rainfall; (

**j**) distance to railway (rail); (

**k**) temperature.

**Figure 7.**The ROC plot and AUC for flood susceptible areas produced by the ANN and LR models: (

**a**) success rate; (

**b**) prediction rate.

Period | Contents of the Data | Data Type | Source |
---|---|---|---|

1985–2020 | Location, date, validation, displaced, deaths, severity | Polygon (points) | EM-DAT, CRED |

1985–2020 | Location, date, affected | Polygon (points) | Dartmouth Flood Observatory (DFO) |

Data | Sources | Format | Period |
---|---|---|---|

Rainfall | Nimet, Nigeria | vector | 1975–2015 |

Temperature | Global Climate data: Worldclim | 1 km | 1975–2017 |

Land cover | Globeland30 | 30 m | 2020 |

Soil *a | The Harmonised World Soil Database v1.2 | vector | - |

Soil *b | Global Hydrological Soil Group- ORNL DAAC | 250 m | 2020 |

Elevation | USGS, Earthexplorer | 30 m | 2015 |

Road network | NASA, Socioeconomic Data and Applications Center; Global Roads Open Access Dataset v1 | vector | 2010 |

Rail network | OCHA, Nigeria | vector | 2009 |

Water areas | OCHA, Nigeria | vector | 2010 |

Parameters | Model Values | |
---|---|---|

ANN | Logistic | |

Training | 70 | 70 |

Testing | 30 | 30 |

Number of hidden layers | 8 | 0 |

Number of neurons | 64 | 0 |

Activation function | logistic | logistic |

Learning rate | 0.001 | 0.001 |

Architecture selection | Trial-and-error | Trial-and-error |

Factor | β Coefficient | Significance (p-Value) |
---|---|---|

Aspect | −0.0012 | 0.0022 ** |

Curve Number | 0.0190 | 0.0219 * |

Curvature | 0.0009 | 0.0005 *** |

Elevation | −0.0004 | 0.3717 |

Land use | −0.0076 | 0.0022 ** |

Rainfall | 0.0001 | 0.0075 ** |

Roughness | 0.0015 | 0.0028 ** |

Soil type | −0.0043 | 0.035 * |

Slope | 0.0007 | 0.0945 * |

SPI | −0.2856 | 0.0271 * |

Temperature | −0.0163 | 0.3035 |

TWI | 0.0027 | 0.0934 * |

Distance to Water | 0.0728 | 0.008 ** |

Distance to Road | −0.2047 | 0.0002 *** |

Distance to Railway | 0.0397 | 0.7256 |

**Table 5.**Pearson’s correlation coefficients and multicollinearity results for the selected conditioning factors.

Conditioning Factor | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 1.00 | ||||||||||||||

2 | −0.03 | 1.00 | |||||||||||||

3 | 0.58 | −0.01 | 1.00 | ||||||||||||

4 | 0.04 | 0.00 | −0.06 | 1.00 | |||||||||||

5 | 0.02 | −0.03 | 0.05 | −0.02 | 1.00 | ||||||||||

6 | −0.03 | 0.03 | −0.01 | 0.34 | 0.01 | 1.00 | |||||||||

7 | −0.03 | 0.01 | −0.05 | −0.65 | 0.10 | 0.20 | 1.00 | ||||||||

8 | 0.00 | 0.04 | 0.04 | 0.36 | −0.04 | 0.07 | 0.22 | 1.00 | |||||||

9 | 0.03 | −0.01 | 0.02 | 0.02 | −0.02 | 0.02 | 0.14 | −0.01 | 1.00 | ||||||

10 | 0.00 | −0.04 | −0.05 | −0.18 | −0.13 | 0.20 | −0.06 | 0.08 | 0.14 | 1.00 | |||||

11 | 0.20 | −0.03 | −0.11 | 0.01 | 0.00 | 0.04 | 0.00 | −0.07 | 0.01 | 0.01 | 1.00 | ||||

12 | −0.04 | −0.04 | −0.02 | 0.06 | −0.01 | −0.03 | 0.09 | 0.00 | 0.03 | 0.00 | 0.02 | 1.00 | |||

13 | 0.05 | −0.36 | 0.00 | −0.07 | −0.02 | −0.01 | −0.04 | −0.06 | 0.10 | −0.05 | −0.08 | −0.03 | 1.00 | ||

14 | −0.05 | 0.08 | 0.03 | −0.10 | −0.13 | 0.00 | −0.33 | −0.09 | 0.02 | 0.05 | 0.00 | 0.02 | −0.04 | 1.00 | |

15 | 0.16 | −0.02 | −0.10 | 0.04 | 0.03 | 0.03 | 0.10 | 0.01 | −0.02 | −0.02 | 0.05 | −0.01 | 0.00 | 0.17 | 1.00 |

VIF | 1.64 | 1.18 | 1.56 | 2.25 | 1.09 | 1.24 | 2.13 | 1.27 | 1.08 | 1.12 | 1.06 | 1.02 | 1.21 | 1.25 | 1.07 |

Model Parameters | ANN | LR | ||
---|---|---|---|---|

Training | Testing | Training | Testing | |

MSE | 0.047 | 0.035 | 0.195 | 0.107 |

RMSE | 0.217 | 0.188 | 0.442 | 0.327 |

AUC | 0.964 | 0.764 | 0.677 | 0.625 |

Accuracy | 0.907 | 0.875 | 0.772 | 0.784 |

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## Share and Cite

**MDPI and ACS Style**

Ighile, E.H.; Shirakawa, H.; Tanikawa, H.
Application of GIS and Machine Learning to Predict Flood Areas in Nigeria. *Sustainability* **2022**, *14*, 5039.
https://doi.org/10.3390/su14095039

**AMA Style**

Ighile EH, Shirakawa H, Tanikawa H.
Application of GIS and Machine Learning to Predict Flood Areas in Nigeria. *Sustainability*. 2022; 14(9):5039.
https://doi.org/10.3390/su14095039

**Chicago/Turabian Style**

Ighile, Eseosa Halima, Hiroaki Shirakawa, and Hiroki Tanikawa.
2022. "Application of GIS and Machine Learning to Predict Flood Areas in Nigeria" *Sustainability* 14, no. 9: 5039.
https://doi.org/10.3390/su14095039