TY - JOUR
T1 - Enhancing daily rainfall prediction in urban areas
T2 - a comparative study of hybrid artificial intelligence models with optimization algorithms
AU - Sheikhi, Yaser
AU - Ashrafi, Seyed Mohammad
AU - Nikoo, Mohammad Reza
AU - Haghighi, Ali
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715.
AB - Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715.
KW - Data-driven approach
KW - Flood prediction
KW - Hybrid models
KW - Rainfall
KW - Wavelet transformation
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UR - https://www.mendeley.com/catalogue/dcd0e0a6-e211-3dea-8ebf-d29739896a45/
U2 - 10.1007/s13201-023-02036-8
DO - 10.1007/s13201-023-02036-8
M3 - Article
AN - SCOPUS:85176043668
SN - 2190-5487
VL - 13
JO - Applied Water Science
JF - Applied Water Science
IS - 12
M1 - 232
ER -