Flood Prediction Using Machine Learning Models: Literature Review
Abstract
:1. Introduction
2. Method and Outline
3. State of the Art of ML Methods in Flood Prediction
3.1. Artificial Neural Networks (ANNs)
3.2. Multilayer Perceptron (MLP)
3.3. Adaptive Neuro-Fuzzy Inference System (ANFIS)
3.4. Wavelet Neural Network (WNN)
3.5. Support Vector Machine (SVM)
3.6. Decision Tree (DT)
3.7. Ensemble Prediction Systems (EPSs)
3.8. Classification of ML Methods and Applications
4. Short-Term Flood Prediction with ML
4.1. Short-Term Flood Prediction Using Single ML Methods
4.2. Short-Term Flood Prediction Using Hybrid ML Methods
4.3. Comparative Performance Analysis
5. Long-Term Flood Prediction with ML
5.1. Long-Term Flood Prediction Using Single ML Methods
5.2. Long-Term Flood Prediction Using Hybrid ML Methods
6. Comparative Performance Analysis and Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclatures
WMO | World meteorological organization |
GCM | Global circulation models |
SPOTA | Seasonal Pacific Ocean temperature analysis |
ANN | Artificial neural networks |
POTA | Pacific Ocean temperature analysis |
QPE | Quantitative precipitation estimation |
CLIM | Climatology average method |
EOF | Empirical orthogonal function |
MLR | Multiple linear regressions |
QPF | Quantitative precipitation forecasting |
MNLR | Multiple nonlinear regressions |
ML | Machine learning |
MLR | Multiple linear regression |
ANN | Neural networks |
WNN | Wavelet-based neural network |
ARIMA | Auto regressive integrated moving average |
USGS | United States Geological Survey |
FFA | Flood frequency analyses |
QRT | Quantile regression techniques |
SPOTA | Seasonal Pacific Ocean temperature analysis |
SVM | Support vector machines |
LS-SVM | Least-square support vector machines |
AI | Artificial intelligence |
VRM | Vector Regression Machine |
FFNN | Feed-forward neural network |
FBNN | Feed-backward networks |
MLP | Multilayer perceptron |
ANFIS | Adaptive neuro-fuzzy inference system |
BPNN | Backpropagation neural network |
SVR | Support vector regression |
LR | Linear regression |
FIS | Fuzzy inference system |
CART | Classification and regression tree |
LMT | Logistic model trees |
NWP | Numerical weather prediction |
NBT | Naive Bayes trees |
ARMA | Autoregressive moving averaging |
REPT | Reduced-error pruning trees |
DT | Decision tree |
ELM | Extreme learning machine |
EPS | Ensemble prediction systems |
SNIP | Source normalized impact per paper |
SRM | Structural risk minimization |
AR | Autoregressive |
SJR | SCImago journal rank |
ARMAX | Linear autoregressive moving average with exogenous inputs |
LMT | Logistic model trees |
ARMA | Autoregressive moving averaging |
ADT | Alternating decision trees |
NARX network | Nonlinear autoregressive network with exogenous inputs |
RMSE | Root-mean-square error |
RFFA | Regional flood frequency analysis |
NLR | Nonlinear regression |
AR | Autoregressive |
WARM | Wavelet autoregressive model |
NLR-R | Nonlinear regression with regionalization approach |
E | Nash Sutcliffe index |
FR | Frequency ratio |
SOM | Self-organizing map |
CHIM | Cluster-based hybrid inundation model |
FFRM | Flash flood routing model |
KGE | Kling-Gupta efficiency |
AME | ANN-based monsoon rainfall enhancement |
SSNN | State-space neural network |
SSL | Suspended sediment load |
NSE | Nash–Sutcliffe efficiency |
E-CHAID | Exhaustive CHAID |
CHAID | Chi-squared automatic interaction detector |
CLIM | Climatology average model |
HEC–HMS | Hydrologic engineering left–hydrologic modeling system |
SOM | Self-organizing map |
PBIAS | Percent bias |
NLPM | Nonlinear perturbation model |
RF | Rotation forest |
KSOFM-NNM | Kohonen self-organizing feature maps neural networks model |
DBP | Division-based backpropagation |
DBPANN | DBP neural network |
NLPM-ANN | Nonlinear perturbation model based on neural network |
GRNNM | Generalized regression neural networks model |
IIS | Iterative input selection |
EEMD | Ensemble empirical mode decomposition |
ANNE | Artificial neural network ensembles |
DWT | Discrete wavelet transform |
SFF | Seasonal flood forecasting |
MP | Water monitoring points |
WBANN | Wavelet–bootstrap–ANN |
HBI | Hilsenhoff’s biotic index |
RT | Regression trees |
EMD | Empirical mode decomposition |
LLR | Local linear regression |
BFGS | Broyden Fletcher Goldfarb Shanno |
M-EMD | Modified empirical mode decomposition |
IIS | Iterative input selection |
SAR | Seasonal first-order autoregressive |
BFGSNN | Broyden Fletcher Goldfarb Shanno neural network |
GRNN | Artificial neural networks including generalized regression network |
T–S | Takagi–Sugeno |
WLGP | Wavelet linear genetic programming |
E | Nash coefficients |
TSC-T–S | Clustering based Takagi–Sugeno |
TCs | Tropical cyclones |
PCA | Principal component analysis |
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Modeling Technique | Reference | Flood Resource Variable | Prediction Type | Region |
---|---|---|---|---|
ANN vs. statistical | [1] | Streamflow and flash food | Hourly | USA |
ANN vs. traditional | [44] | Water and surge level | Hourly | Japan |
ANN vs. statistical | [149] | Flood | Real-time | UK |
ANN vs. statistical | [150] | Extreme flow | Hourly | Greece |
FFANN vs. ANN | [151] | Water level | Hourly | India |
ANN vs. T–S | [4] | Flood | Hourly | India |
ANN vs. AR | [153] | Stage level and streamflow | Hourly | Brazil |
MLP vs. Kohonen NN | [154] | Flood frequency analysis | Long-term | China |
BPANN | [155] | Peak flow of flood | Daily | Canada |
BPANN vs. DBPANN | [156] | Rainfall–runoff | Monthly and daily | China |
BPANN | [157] | Flash flood | Real-time | Hawaii |
BPANN | [158] | Runoff | Daily | India |
ELM vs. SVM | [159] | Streamflow | Daily | China |
BPANN vs. NARX | [160,161] | Urban flood | Real-time | Taiwan |
FFANN vs. Functional ANN | [162] | River flows | Real-time | Ireland |
Recurrent NN vs. Z–R relation | [163] | Rainfall prediction | Real-time | Taiwan |
ANN vs. M5 model tree | [164] | Peak flow | Hourly | India |
NBT vs. DT vs. Multinomial regression | [165] | Flash flood | Real-time, hourly | Austria |
DTs vs. NBT vs. ADT vs. LMT, and REPT | [166] | Flood | Hourly/daily | Iran |
MLP vs. MLR | [167,168] | River flow and rainfall–runoff | Daily | Algeria |
MLP vs. MLR | [98] | River runoff | Hourly | Morocco |
MLP vs. WT vs. MLR vs. ANN | [169] | River flood forecasting | Daily | Canada |
ANN vs. MLP | [170] | River level | Hourly | Ireland |
MLP vs. DT vs. CART vs. CHAID | [171] | Flood during typhoon | Rainfall–runoff | China |
SVM vs. ANN | [120] | Rainfall extreme events | Daily | India |
ANN vs. SVR | [48] | Flood | Daily | Canada |
RF vs. SVM | [69] | Rainfall | Hourly | Taiwan |
Modeling Technique | Complexity of Algorithm | Ease of Use | Speed | Accuracy | Input Dataset |
---|---|---|---|---|---|
ANN | High | Low | Fair | Fair | Historical |
BPANN | Fairly high | Low | Fairly high | Fairly high | Historical |
MLP | Fairly high | Fair | High | Fairly high | Historical |
ELM | Fair | Fairly high | Fairly high | Fair | Historical |
CART | Fair | Fair | Fair | Fairly high | Historical |
SVM | Fairly high | Low | Low | Fair | Historical |
ANFIS | Fair | Fairly high | Fair | Fairly high | Historical |
Modeling Technique | Reference | Flood resource Variable | Prediction Type | Region |
---|---|---|---|---|
ANFIS vs. ANN | [174] | Flash floods | Real-time | Spain |
ANFIS vs. ANN | [175,176] | Water level | Hourly | Taiwan |
ANFIS vs. ANN | [46] | Watershed rainfall | Hourly | Taiwan |
ANFIS vs. ANN | [67] | Flood quantiles | Real-time | Canada |
ANN vs. ANFIS | [177] | Daily flow | Daily | Iran |
CART vs. ANFIS vs. MLP vs. SVM | [134] | Sediment transport | Daily | Iran |
MLP vs. GRNNM vs. NNM | [96] | Flood prediction | Daily | Korea |
SVM-FR vs. DT | [178] | Rainfall–runoff | Real-time | Malaysia |
HEC–HMS–ANN vs. HEC–HMS-SVR | [179] | Rainfall–runoff | Hourly | Taiwan |
SAS–MP vs. W-SAS–MP | [180] | Flash flood and streamflow | Daily | Turkey |
SOM–R-NARX vs. R-NARX | [181] | Regional flood | Hourly | Taiwan |
Wavelet-based NARX vs. ANN, vs. WANN | [182] | Streamflow forecasting | Daily | India |
WBANN vs. WANN vs. ANN vs. BANN | [105] | Flood | Hourly | India |
ANN–hydrodynamic model | [183] | Flood prediction: tidal surge | Hourly | UK |
RNN–SVR, RSVRCPSO | [184] | Flash flood: rainfall forecasting | Hourly | Taiwan |
AME and SSNN vs. ANN | [185] | Rainfall forecasting | Hourly | Taiwan |
Hybrid of FFNN with linear model | [186] | Flood forecasting: daily flows | Daily | India |
FFNN vs. FBNN vs. FFRM–ANN | [187] | Flash floods | Hourly | Taiwan |
ANN–NLPM vs. ANN | [188] | Rainfall–runoff | Daily | China |
EPS of MLP vs. SVM vs. RF | [189] | Runoff simulations | Real-time | Germany |
EPS of ANNs | [190] | Flood | Daily | Canada |
Modeling Technique | Reference | Flood Resource Variable | Prediction Type | Region |
---|---|---|---|---|
ANNs | [197] | Water levels | Seasonal | Sudan |
ANNs | [87] | Precipitation | Monthly | Australia |
BPNNs | [199] | Heavy rainfall | Seasonal | India |
BPNNs vs. BFGSNN | [200] | Reservoir levels | Monthly | Turkey |
BPNN vs. MLP | [201] | Discharge | Monthly | Iran |
ANNs vs. HBI | [202] | Stream | Weekly | Canada |
SVM vs. ANN | [203] | Streamflow | Monthly | China |
RT | [204] | Floodplain forests | Annually | Australia |
Modeling Technique | Complexity of Algorithm | Ease of Use | Speed | Accuracy | Input Dataset |
---|---|---|---|---|---|
ANN | Fairly high | Low | Fair | High | Historical |
BPNN | Fairly high | Low | Fairly high | Fairly high | Historical |
MLP | high | Fair | High | Fairly high | Historical |
SVR | Fairly high | Low | Low | High | Historical |
RT | Fair | Fair | Fair | Fairly high | Historical |
SVM | Fairly high | Low | Low | High | Historical |
M5 tree | Fair | Low | Fair | Fair | Historical |
Modeling Technique | Reference | Flood Resource Variable | Prediction Type | Region |
---|---|---|---|---|
Autoregressive ANN vs. ARMA vs. ARIMA | [26] | River inflow | Monthly and yearly | Iran |
Hybrid WNN vs. M5 model tree | [206] | Streamflow water level | Monthly | Australia |
WNN vs. ANN | [207,208] | Rainfall–runoff | Monthly | Italy |
WNN-BB vs. WNN vs. ANN | [50] | Streamflow | Weekly and few days | Canada |
WNN vs. ANN | [25] | Urban water | Monthly | Canada |
WNN vs. ANN | [209] | Peak flows | Seasonal | India |
WNN vs. ANN | [210] | Rainfall | Monthly | India |
WARM vs. AR | [211] | Rainfall | Yearly | Thailand |
ANFIS vs. ANNs | [212] | Rainfall | Seasonal | Australia |
ANFIS vs. ARMA vs. ANNs vs. SVM | [213] | Discharge | Monthly | China |
ANFIS, ANNs vs. SVM vs. LLR | [214] | Streamflow | Short-term | Turkey |
NLPM–ANN | [215] | Flood forecasting | Yearly | China |
M-EMDSVM vs. ANN vs. SVM | [216] | Streamflow | Monthly | China |
SVR–DWT–EMD | [217] | Streamflow | Monthly | China |
Surrogate modeling–ML vs. ANN–Kriging model vs. ANN–PCA | [218] | Rainfall–runoff | Yearly | USA |
EPS of ANNs: K-NN vs. MLP vs. MLP–PLC vs. ANNE | [219] | Streamflow | Seasonal | Canada |
EEMD–ANN vs. SVM vs. ANFIS | [220] | Runoff forecast | Monthly | China |
WNN vs. ANN vs. WLGP | [51] | Streamflow | Monthly | Iran |
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Mosavi, A.; Ozturk, P.; Chau, K.-w. Flood Prediction Using Machine Learning Models: Literature Review. Water 2018, 10, 1536. https://doi.org/10.3390/w10111536
Mosavi A, Ozturk P, Chau K-w. Flood Prediction Using Machine Learning Models: Literature Review. Water. 2018; 10(11):1536. https://doi.org/10.3390/w10111536
Chicago/Turabian StyleMosavi, Amir, Pinar Ozturk, and Kwok-wing Chau. 2018. "Flood Prediction Using Machine Learning Models: Literature Review" Water 10, no. 11: 1536. https://doi.org/10.3390/w10111536
APA StyleMosavi, A., Ozturk, P., & Chau, K. -w. (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536