# The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed

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## Abstract

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^{2}(0.9537), NSE (0.9292), and RMSE (0.3701) validation performances. The ANN model was tested with a new dataset to demonstrate the capability of predicting flood inundation height with unseen data. Such a data-driven approach is a promising tool to be developed to reduce flood risks in the Majalaya Watershed and other flood-prone locations.

## 1. Introduction

^{3}/s. As a comparison, the typical maximum daily rainfall recorded was in the 70–90 mm/day range. From crowdsourced information, this flood created 5.35 km

^{2}of inundation [9].

## 2. Study Area

#### 2.1. Area Orientation

#### 2.2. River System

## 3. Materials and Methods

#### 3.1. General Procedure

#### 3.2. Materials

**Figure 3.**Methodology from data input preparation until running the ANN model for predicting the inundation height. The input variables used were satellite rainfall, inundation height, distance to the river, distance to river inflow, and elevation.

#### 3.2.1. Rainfall Data

#### 3.2.2. Water Level Data

#### 3.2.3. Topography Data

#### 3.3. Modeling and Simulation

#### 3.3.1. HEC-RAS Simulation and Flood Event Selection

^{3}/s as the full riverbank capacity outlined by the previous research [9]. The total number of events from 2017 until 2022 that discharged more than 100 m

^{3}/s was sixteen events, with the largest discharge being 224.64 m

^{3}/s in March 2020. The period for one event started from the discharge rise until it returned to the normal level, forming a hydrograph. The hydrographs were used to calculate each tributary hydrograph and then used as inputs to the HEC-RAS model. The HEC-RAS model was run on a computer server PC with a 16-core 32-thread Ryzen 9 5950X CPU with 96 GB of DDR4 memory and an NVME SSD.

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

## 4. Results

#### 4.1. Flood Inundation Height

**Figure 4.**(

**A**) The Euclidean distance for the Citarum and Cisunggalah Rivers; (

**B**) Flood inundation map from the HEC-RAS simulation used for input data in the machine learning model; (

**C**) the Euclidean distance for the inflow of the Citarum and Cisunggalah Rivers; (

**D**) Satellite rainfall map for the Majalaya Watershed.

#### 4.2. Variable Correlation

^{2}). The coefficients are widely used for regression analysis evaluation, identifying how much the independent variable determines the dependent variable [46]. The coefficients of correlation of the four input or explanatory variables and one output variable were processed using R language, with the “cor” command in the “tidyverse” library. Knowing the correlation between the explanatory and the output variables is important for understanding the character of the variables and ensuring that no variables are biased. The correlations between all variables in the ANN model are shown in Figure 5. The correlation between “distance to inflow variable” and “distance to the nearest river” had the highest coefficient of determination, −0.696. The negative value indicates that the independent variable (distance to inflow) was not determined by the dependent variable (distance to the nearest river).

#### 4.3. Neural Network Model

^{2}values were calculated to measure the relationship between the two data. The R

^{2}for the training subset was 0.9529. This shows that the predicted inundation height was influenced by 95% by the explanatory variables for the training subset. The inundation height prediction Nash–Sutcliffe Efficiency (NSE) score was 0.9292, and the root-mean-square error (RMSE) was 0.3701.

^{2}value for the validation subset was 0.9537, meaning the predicted inundation height of the validation subset was also 95% influenced by the explanatory variables. As the two data types had a high correlation value greater than 0.8, the neural network model can be deemed successful in predicting the inundation height.

#### 4.4. Testing

## 5. Discussion

## 6. Conclusions

^{2}, NSE, and RMSE values. The ANN used four explanatory variables: rainfall, distance to river inflow, distance to the nearest river, and elevation. The ANN model successfully predicted the inundation height with R

^{2}values of 0.9529 for training and 0.9537 for validation. The ANN model was applied to predict the inundation height with new input data, and the model predicted the inundation height and the inundated location. Finally, it was concluded that the ANN model is a promising tool for early warning systems. Further research avenues are advised, e.g., utilizing various real-time data such as climate data, land or soil data, and demographic data for early warning systems. Since this study is limited to the flood-prone area in Majalaya and short-range historical data (rainfall data and selected events), the results presented in this study are expected to be reproducible in other locations and generalizable to other cases.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Majalaya Watershed area and Citarum River in West Java; the green point is where Majalaya’s Automatic Water Level Recorder (AWLR) is located.

**Figure 6.**The trained ANN architecture scheme, showing the involved explanatory variables, the target variable (inundation height), and the link weights. The circles with the number “1” inside represent bias nodes.

**Figure 7.**The inundation height of the modeling training data (

**A**) and validation data (

**B**) using an artificial neural network compared with observed inundation height from HEC-RAS. Parts (

**C**) and (

**D**) show a comparison between the HEC-RAS and ANN inundation results for a selected event, with part (

**E**) presenting the difference between the two results.

**Figure 8.**(

**A**) Inundation height prediction map, the numbers indicate the testing event number; (

**B**) Inundation height point overlay with the nearest river Euclidean distance; (

**C**) Inundation height point overlay with the inflow river Euclidean distance.

River | Area (km^{2}) |
---|---|

Citangkurak | 1.29 |

Cipalemahan | 7.69 |

Cihampelas | 3.47 |

Cikaro | 30.95 |

Cisunggalah | 45.83 |

Citarum | 165.09 |

Total | 207 |

Data Name | Data Year | Source |
---|---|---|

Majalaya area DEM | 2018 | DEMNAS (Indonesian National DEM data) from BIG |

River bathymetry | 2018 | Primary survey data |

Flood event boundaries | 2018 | Crowdsourced data |

RBI land cover map | 2006 | BIG |

Satellite rainfall | 2014–2022 | JAXA (Japan Aerospace Exploration Agency) |

Rain gauge precipitation | 2014–2022 | BMKG (Indonesian Meteorology, Climatology, and Geophysics Agency) |

Citarum River discharge | 2017–2022 | BBWS Citarum (Citarum River Authority) |

No. | X (m) | Y (m) | Rainfall (Hourly) | Distance to Inflow (m) | Distance to River (m) | Elevation (m) | Inundation Height Prediction (m) |
---|---|---|---|---|---|---|---|

1 | 805,914.90 | 9,223,578.28 | 5.64 | 3044.16 | 14.66 | 662.15 | 0.83 |

2 | 806,487.38 | 9,222,003.97 | 6.87 | 1472.41 | 6.90 | 666.47 | 0.69 |

3 | 806,000.78 | 9,223,492.41 | 31.23 | 2959.83 | 37.49 | 662.44 | 0.80 |

4 | 805,829.03 | 9,219,799.95 | 4.50 | 169.87 | 883.42 | 675.69 | 0.75 |

5 | 805,628.67 | 9,219,599.58 | 9.33 | 438.76 | 621.37 | 676.03 | 0.30 |

6 | 804,455.09 | 9,218,855.36 | 4.73 | 891.40 | 480.27 | 672.05 | 2.66 |

7 | 804,340.60 | 9,220,143.43 | 47.84 | 1487.54 | 7.29 | 670.08 | 1.85 |

8 | 806,458.75 | 9,221,889.48 | 8.08 | 1360.26 | 91.94 | 666.00 | 0.96 |

9 | 806,258.39 | 9,222,175.72 | 7.83 | 1660.24 | 53.87 | 666.00 | 0.72 |

10 | 805,027.57 | 9,218,111.14 | 2.80 | 27.39 | 345.42 | 683.87 | 1.13 |

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**MDPI and ACS Style**

Burnama, N.S.; Rohmat, F.I.W.; Farid, M.; Kuntoro, A.A.; Kardhana, H.; Rohmat, F.I.W.; Wijayasari, W.
The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed. *Water* **2023**, *15*, 3026.
https://doi.org/10.3390/w15173026

**AMA Style**

Burnama NS, Rohmat FIW, Farid M, Kuntoro AA, Kardhana H, Rohmat FIW, Wijayasari W.
The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed. *Water*. 2023; 15(17):3026.
https://doi.org/10.3390/w15173026

**Chicago/Turabian Style**

Burnama, Nabila Siti, Faizal Immaddudin Wira Rohmat, Mohammad Farid, Arno Adi Kuntoro, Hadi Kardhana, Fauzan Ikhlas Wira Rohmat, and Winda Wijayasari.
2023. "The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed" *Water* 15, no. 17: 3026.
https://doi.org/10.3390/w15173026