# Assessing Coastal Flood Susceptibility in East Java, Indonesia: Comparison of Statistical Bivariate and Machine Learning Techniques

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. Geographically, it was located at 8°00′ S to 7°30′ S and 112°45′ E to 113°30′ E, as shown in Figure 1. The basin comprises many small watersheds that flow into the Java Sea. The basin is formed by three geomorphological units, namely, Mount Arjuno, Bromo, and Argopuro. The highest elevation in this area is 3323 m above sea level (m.a.s.l). The upstream part of the watershed has a steep slope, while the downstream part is relatively flat.

#### 2.2. Methodology

#### 2.2.1. Flood Inventory

#### 2.2.2. Flood Conditioning Factors

_{s}is the upstream contribution area and β is the slope angle value. In Figure 3, the TWI classification has almost the same proportion value for all classes, namely, the range between 15.52% to 18.81%. SPI is related to the strength of the flow in the watershed. The value of the SPI index can be calculated using Equation (2):

^{2}/m) and β represents the slope angle value. SPI classification in the first two classes was the dominance of the SPI values, from the lowest, namely, 40.81% and 57.53%.

^{2}). In Figure 3, the river network density in the study area for each class had almost the same value, i.e., between 16.12 km/km

^{2}and 19.17 km/km

^{2}. The distance to a river can determine the level of susceptibility of an area to flooding. The closer the area is to a river, the more prone to flooding.

#### 2.2.3. Flood Susceptibility Calculation Approach

#### Frequency Ratio Model

_{pix}(FX

_{i}) is the number of pixels with flood events in class i, N

_{pix}(FX

_{j}) is the number of pixels in factor X

_{j}, m is the number of classes in factor Xi, and n is the number of factors in the study area. The flood susceptibility index (FSI) is calculated by adding up all the FR values.

#### Weight of Evidence

^{+}) and negative weights (W

^{−}), as shown in Equations (9) and (10):

^{−}is a negative correlation with the weights indicating the absence of effective factors that condition flooding, and vice versa for W

^{+}[16]. P is the probability; ln is the natural logarithm. A and B are the entire area and the incidence of flooding in each factor class, respectively. A and B, respectively, are all events that are not flooded, and all are not flooded in the class of each factor. The weight contrast is the difference between positive and negative weights. The positive contrast value indicates a positive spatial relationship, while the negative one indicates a negative spatial relationship. The magnitude of this contrast value reflects the overall spatial relationship between each class of factors causing flooding. Furthermore, the standard deviation of the contrast is the combined root of the variance of the weights formulated in Equations (11) and (12):

_{final}is the final weight for the WofE model, which is the ratio between contras and standard deviation:

#### Random Forest (RF)

#### Multi-Layer Perceptron (MLP)

_{ij}is the weighting between matrix X and matrix H (the hidden intermediate matrix), V

_{ij}is the weighting between matrix H and matrix Y, and X

_{T}is the flooding at location T.

#### Model Performance Evaluation

## 3. Result and Discussion

#### 3.1. Multi-Collinearity Test

#### 3.2. FR and WofE Approach

#### 3.3. Information Gain Ratio Test

#### 3.4. MLP and RF Approaches

#### 3.5. Coastal Flood Susceptibility Mapping

#### 3.5.1. Flood Susceptibility Model Performance

#### 3.5.2. Flood Conditioning Factors

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Input thematic layers: (

**a**) elevation, (

**b**) land use, (

**c**) flow accumulation (FA), (

**d**) topographic wetness index (TWI), (

**e**) stream power index (SPI), (

**f**) NDVI, (

**g**) distance to rivers (DR), (

**h**) river density (RD), (

**i**) rainfall intensity, (

**j**) soil type, and (

**k**) geology.

**Figure 4.**Random forest structure [34].

Layer | Factor | Source | Resolution/Scale |
---|---|---|---|

DEM | Elevation | USGS Explore | 30 × 30 m |

Flow accumulation | |||

TWI | |||

SPI | |||

Landsat 8 imagery | NDVI | USGS, 2020 | 30 × 30 m |

River network | River density | Rupa Bumi Indonesia | 1:25,000 |

Distance to the river | |||

Hydro-meteorology | Rainfall | East Java Provincial Public Works Service | 1:25,000 |

Soil | Soil | ESDM Department | 1:250,000 |

Geology | Geology | ESDM Department | 1:250,000 |

Land use | Land use | Rupa Bumi Indonesia | 1:25,000 |

Elevation | SPI | TWI | Density | Landuse | FA | Distance | NDVI | Geology | Soil | |
---|---|---|---|---|---|---|---|---|---|---|

Elevation | ||||||||||

SPI | 0.051 | |||||||||

TWI | −0.185 | 0.376 | ||||||||

Density | −0.014 | −0.039 | −0.141 | |||||||

Landuse | 0.004 | 0.047 | 0.150 | −0.274 | ||||||

FA | −0.040 | 0.714 | 0.634 | −0.052 | 0.140 | |||||

Distance | 0.087 | −0.043 | −0.012 | −0.118 | 0.119 | −0.074 | ||||

NDVI | −0.174 | −0.036 | −0.015 | 0.046 | 0.044 | 0.000 | −0.183 | |||

Geology | 0.302 | −0.020 | −0.114 | 0.097 | −0.135 | −0.042 | −0.006 | −0.027 | ||

Soil | 0.461 | 0.063 | −0.063 | 0.483 | 0.037 | 0.033 | −0.135 | −0.066 | 0.005 | |

Rainfall | 0.279 | −0.014 | −0.128 | −0.029 | −0.040 | −0.039 | 0.007 | −0.211 | 0.520 | −0.013 |

Model | FR | WofE | RF | MLP |
---|---|---|---|---|

Training | 0.926 | 0.925 | 0.939 | 0.967 |

Testing | 0.921 | 0.920 | 0.936 | 0.956 |

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**MDPI and ACS Style**

Hidayah, E.; Indarto; Lee, W.-K.; Halik, G.; Pradhan, B. Assessing Coastal Flood Susceptibility in East Java, Indonesia: Comparison of Statistical Bivariate and Machine Learning Techniques. *Water* **2022**, *14*, 3869.
https://doi.org/10.3390/w14233869

**AMA Style**

Hidayah E, Indarto, Lee W-K, Halik G, Pradhan B. Assessing Coastal Flood Susceptibility in East Java, Indonesia: Comparison of Statistical Bivariate and Machine Learning Techniques. *Water*. 2022; 14(23):3869.
https://doi.org/10.3390/w14233869

**Chicago/Turabian Style**

Hidayah, Entin, Indarto, Wei-Koon Lee, Gusfan Halik, and Biswajeet Pradhan. 2022. "Assessing Coastal Flood Susceptibility in East Java, Indonesia: Comparison of Statistical Bivariate and Machine Learning Techniques" *Water* 14, no. 23: 3869.
https://doi.org/10.3390/w14233869