Peer review of: Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance (2020)

Reviewers' comments: 

 

Reviewer #1 (Remarks to the Author): 

 
1. The manuscript presents excavating typhoon tracks to AI-based flood early warnings, which is 
interesting. The subject addressed is within the scope of the journal. 

2. However, the manuscript, in its present form, contains several weaknesses. Appropriate revisions 
to the following points should be undertaken in order to justify recommendation for publication. 

3. Full names should be shown for all abbreviations in their first occurrence in texts. For example, AI 
in p.1, etc. 

4. For readers to quickly catch your contribution, it would be better to highlight major difficulties 
and challenges, and your original achievements to overcome them, in a clearer way in abstract and 
introduction. 

5. It is shown in the reference list that the authors have several publications in this field. This raises 
some concerns regarding the potential overlap with their previous works. The authors should 
explicitly state the novel contribution of this work, the similarities and the differences of this work 
with their previous publications. 

6. It is mentioned in p.1 that an AI-based intelligent modeling approach is adopted for predictions of 
typhoon path and the potential for flooding. What are the other feasible alternatives? What are the 
advantages of adopting this particular approach over others in this case? How will this affect the 
results? More details should be furnished. 

7. It is mentioned in p.2 that the Shihmen Reservoir watershed is adopted as the case study. What 
are other feasible alternatives? What are the advantages of adopting this particular case study over 
others in this case? How will this affect the results? The authors should provide more details on this. 

8. It is mentioned in p.5 that historical records of 1971 to 2015 are taken. Why are more recent data 
not included in the study? Is there any difficulty in obtaining more recent data? Are there any 
changes to situation in recent years? What are its effects on the result? 

9. It is mentioned in p.5 that total rainfall, typhoon track, the date and time warnings issued, hourly 
reservoir inflow, and outflow peak data are adopted as the data set for the prediction model. What 
are other feasible alternatives? What are the advantages of adopting these parameters over others 
in this case? How will this affect the results? The authors should provide more details on this. 

10. It is mentioned in p.6 that a track vectorization method is adopted to produce digitized typhoon 
tracks. What are the other feasible alternatives? What are the advantages of adopting this particular 
method over others in this case? How will this affect the results? More details should be furnished. 

11. It is mentioned in p7 that self-organized map is adopted to cluster the 87 tracks selected for 
model training into a map. What are the other feasible alternatives? What are the advantages of 
adopting this particular soft computing technique over others in this case? How will this affect the 
results? More details should be furnished. 
12. It is mentioned in p.10 that flow characteristic curve is adopted to provide slopes for the rising 
and recession limbs of the hydrograph. What are the other feasible alternatives? What are the 
advantages of adopting this particular approach over others in this case? How will this affect the 
results? More details should be furnished. 

13. It is mentioned in p.11 that three schemes are adopted to classify flow characteristic curves. 
What are other feasible alternatives? What are the advantages of adopting these schemes over 
others in this case? How will this affect the results? The authors should provide more details on this. 

14. It is mentioned in p.14 that two strategies are adopted for flow characteristic curve prediction. 
What are other feasible alternatives? What are the advantages of adopting these strategies over 
others in this case? How will this affect the results? The authors should provide more details on this. 

15. Some key parameters are not mentioned. The rationale on the choice of the particular set of 
parameters should be explained with more details. Have the authors experimented with other sets 
of values? What are the sensitivities of these parameters on the results? 

16. Some assumptions are stated in various sections. Justifications should be provided on these 
assumptions. Evaluation on how they will affect the results should be made. 

17. The discussion section in the present form is relatively weak and should be strengthened with 
more details and justifications. 

18. Moreover, the manuscript could be substantially improved by relying and citing more on recent 
literatures about contemporary real-life case studies of soft computing techniques in hydrologic 
prediction such as the followings: 

 Yaseen, Z.M., et al., “An enhanced extreme learning machine model for river flow forecasting: 
state-of-the-art, practical applications in water resource engineering area and future research 
direction,” Journal of Hydrology 569: 387-408 2019. 

 Cheng, C.T., et al., “Three-person multi-objective conflict decision in reservoir flood control,” 
European Journal of Operational Research 142 (3): 625-631 2002. 

 Moazenzadeh, R., et al., “Coupling a firefly algorithm with support vector regression to predict 
evaporation in northern Iran,” Engineering Applications of Computational Fluid Mechanics 12 (1): 
584-597 2018. 

 Wu, C.L., et al., “Rainfall-Runoff Modeling Using Artificial Neural Network Coupled with Singular 
Spectrum Analysis”, Journal of Hydrology 399 (3-4): 394-409 2011. 

 Ghorbani, M.A., et al., “Forecasting pan evaporation with an integrated Artificial Neural Network 
Quantum-behaved Particle Swarm Optimization model: a case study in Talesh, Northern Iran,” 
Engineering Applications of Computational Fluid Mechanics 12 (1): 724-737 2018. 

 Chau, K.W., et al., “Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling” Water 9(3): 
article no. 186, 6p 2017. 

19. In the conclusion section, the limitations of this study, suggested improvements of this work and 
future directions should be highlighted. 

Reviewer #2 (Remarks to the Author): 

Review of Chang et al. 

General comments: this is a very interesting study that explores the use of AI/SOM clustering for 
typhoon-induced flooding forecasting. However, there are several unanswered critical issues and 
overstatements that need to be addressed before acceptance can be recommended: 


1. It is unclear how the authors can claim to have improved predictions of the typhoon tracks. 
Categorization of the CWB forecast track into one of the clusters does not by itself make the track 
forecast better. The 3 forecast examples happened to be the cases that the CWB forecast tracks are 
reasonably predicted. What if the CWB track is incorrectly forecasted to a significantly different 
neurons? Can the authors show how bad if such a bad forecast will lead to flood forecasting? 

 

2. More generally, given the inherent uncertainties in typhoon track forecasting, and the critical 
dependence of flooding on typhoon track and duration forecasting, it is unclear how the proposed 
method will incorporate the forecast uncertainties as now most operational weather prediction 
centers are moving from deterministic (single-track) forecasting to probabilistic (ensemble) 
forecasting? One example of such diverse track uncertainty and the consequence to extreme 
flooding forecasting can be seen for Typhoon Morakot (2009), see reference such as Zhang et al. 
(2010, Weather and Forecasting, page 1816-1825). 

 

3. The usage of the proposed method appears to be rather limited to the 2-day range of forecast 
lead time as the past analog tracks shown in Fig.2a. How can the method be extended to longer lead 
times since in many remote areas or reservoir planning a 2-day lead time warning will not be 
sufficient for evacuation and flood control? With a potential big domain than Figure 2a needs to be 
considered, would this make the SOM approach less concentrated since there are a lot more 
potential track scenarios with longer lead times? 

 

4. It is unclear how the authors can claim the new method improves over the existing warning 
system by a longer lead time for focused flood defense. What is the existing system, and how to 
systematically validate your claims? 

 

5. More philosophically, how can be the proposed technique to be generalizable for other 
applications, in other areas, and with the inclusion of probabilities that are crucial to any extreme 
events forecasting? 


Reviewer #3 (Remarks to the Author): 

This paper uses a method of analogs to predict typhoon tracks and flooding properties. In particular, 
they preprocess the historical track data using self-organized maps, a method from the field of 
artificial intelligence (AI). The idea of applying algorithms from the field of AI to these important 
problems of typhoon and flood prediction is very worthy and promising. 

 

The paper is technically solid, indicating careful application of these methods and including detailed 
explanation of each step, including how they digitally represented the typhoon track data. 

 

This reviewer will abstain from commenting on the quality of the results in the field of flood 
prediction, not being an expert in the area. It is difficult to draw conclusions without an empirical 
comparison to other approaches to this task (including a simple baseline) from the relevant typhoon 
and flood literature. 

 

From the AI perspective, while the AI tools seem to have been properly applied, the choice of AI 
tools would need to be justified via empirical comparisons to other methods used in the literature. 
For example, extreme storm track prediction has recently been done by a variety of supervised 
machine learning techniques, such as convolutional neural networks, using both storm track data 
and SST reanalysis data (wind, pressure) around the storm. 


Reviewer #4 (Remarks to the Author):
This study presents an innovative way to forecast flow to the reservoir based on predicted typhoon tracks and self-organizing map (SOM), flow characteristic curve, and flood hydrograph.
The proposed approach can provide early warning for several days ahead of typhoon landfall; the process for flood prediction can be updated during typhoon period. The results show great potential for flood forecasting with long lead time. The manuscript is well written and will be of interest to readers of Nature Research. I recommend acceptance.
A few comments are listed below:
1. The proposed approach is based on historical typhoon tracks, rainfall pattern, and flow hydrograph. It is assumed that the typhoon tracks, rainfall, and flow are provided from historical record. However, the effectiveness of the method is highly relevant to its ability to predict future rainfall pattern, including the duration, intensity, and total amount. The rainfall pattern for each typhoon event can be unique. It is still a challenge for a numerical weather prediction model to make correct rainfall forecast for the next few days.
2. Flow characteristic curves (FCCs) are estimated using the total flow volume which is converted directly from total rainfall. The total rainfall for an event is not available before the typhoon event ended. The forecasted rainfall a few days before the event ended can vary greatly from the observed value. Similarly, the FCC is estimated based on the start rising limb characteristics and the cessation of rainfall. If the end time for the event is not forecasted correctly, the uncertainty of the forecasted flood hydrograph can be high.
3. Figure 3: the quality of the figure should be improved.
FINALLY: the title of the paper does not make sense to me. I Suggest revision, should be accepted. The definition of “Excavate...to...” may be unclear to readers of Nature Research.