**7. Concluding Remarks**

In flash flood management studies, it is required to use accurate flash flood susceptibility maps by governing bodies and the policy makers for better flash flood mitigation and systematic development of the area. Since recent decades, a large number of methodologies have been developed to improve the accuracy of such maps. In this study, we proposed five new hybrid machine learning computational approaches to predict the possibility of flash flood occurrences in a studied catchment of Iran, where devastating flash flood events are frequent. The proposed methods are four hybrid models: ABM-CDT, Bag-CDT, Dag-CDT, MBAB-CDT, and single classifier: CDT. To construct the flash flood map, in total nine flash flood conditioning factors were taken into consideration to train and test the proposed models. Correlation based feature selection method was used to validate and select the important factors and also to asses relative importance of these factors for modeling of flash floods. Analysis shows that the lowest AM value (0.007) is of distance to fault and the highest AM value (0.608) is of distance to rivers. Distance to faults was then removed from the datasets for the flash flood modeling. Therefore, in the present study, we have considered only eight factors (distance from river, aspect, elevation, slope, rainfall, soil types, land use, and lithology) in the modeling.

The results show that performance of all the studied models in terms of accuracy was good as these models show very low RMSE values and a high percentage of AUC. Results indicate very high AUC value during both training phase (ABM-CDT = 0.995; Bag-CDT = 0.972; Dag-CDT = 0.958; MBAB-CDT = 0.983; and CDT = 0.933) and testing phase (ABM-CDT = 0.96; Bag-CDT = 0.93; Dag-CDT = 0.95; MBAB-CDT = 0.933; and CDT = 0.90). Among all five models, ABM-CDT shows the maximum level of accuracy compared with other models. Evaluation of the FR data of the historical flash flood locations and generated flash flood maps was done for the very high susceptible pixel class. The maximum frequency ratio was observed for ABM-CDT (3.46), followed by Bag-CDT (3.44); Dag-CDT (3.5); CDT (2.88), and MBAB-CDT (2.65) which clearly indicated higher degree of reliability of ABM-CDT and Bag-CDT algorithms. The models, as an outcome of the study, would also help in the development of accurate flash flood susceptible maps in other watersheds of Iran. However, in the model studies, physical link between cause and effect is to be maintained considering local geo-environmental and hydrological factors for better flash flood prediction and management.

In this study, we performed a systematic analysis using multisource geospatial data; a significant number of limitations still exist in this study about data configuration. We have used 12.5 m spatial resolution ALOS-PALSER DEM which is freely available; a higher resolution DEM can provide a more reliable flood map which may be more useful for the practical use of flood mitigation. In addition, feature selection method such as Information Gain should be applied to evaluate the importance of input factors used for better investigation and application of the machine learning models. Furthermore, despite employing robust methodologies, our study area is local in nature. Therefore, this study is required to be extended to other places for the evaluation of its practical application in different terrains and environments.

In this study we did not consider dynamic changes which may be induced by human activities in the form of land use changes, topography alteration, infrastructure development, as well as climate change. These changes may affect the natural hydrological cycle and thus the pattern of floods, in particular of flash flood in urban areas impacting the life and property of communities affected. Another limitation of the model study is the lack of dynamic consideration of changing parameters related with physical changes, flow levels, direction, erosion, sedimentation, blocking of the drainage system, etc. on flood simulation and its causative effect on land development and flood management.

However, there is a great scope for further research related with the assessment, prediction, and mapping of flash floods by applying other combinations of hybrid artificial intelligence models in different areas using high resolution geo-spatial data for better production of flash flood susceptibility maps.

**Author Contributions:** Conceptualization, B.T.P., M.A., L.S.H., and N.A.-A.; data curation, S.J., F.J., and S.K.B.; methodology, N.A.-A., A.A., T.V.P., H.L.V., and B.T.P.; visualization, T.V.P., H.V.L., S.J., S.D., and S.K.B.; writing—original draft preparation, all authors; writing—review and editing, B.T.P., M.A., A.A., F.J., and N.A.-A.; supervision, N.A.-A., M.A., T.V.P., B.T.P. and I.P.; funding acquisition, B.T.P. and N.A.-A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105.08-2019.03.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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