Flood Prediction Using Machine Learning Models: Literature Review

Amir Mosavi; Kwok-wing Chau; Pinar Ozturk

REVIEW 4 major objections 7 minor 228 references

Flood Prediction Using Machine Learning Models: Literature Review

T0 review · 4 major / 7 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A systematic review of 180 comparative studies claims hybrid and ensemble machine-learning models—especially ANFIS, wavelet neural networks, and decomposition-based hybrids—are the most promising for flood prediction, with hybridization…

desk verdict A useful catalogue and a sensible single/hybrid taxonomy, but the central 'most promising methods' ranking is built on pooling RMSE and R2 values that are not commensurable across studies. read the letter →

arxiv 1908.02781 v1 pith:UX3FYKHE submitted 2019-08-07 cs.LG stat.ML

Amir Mosavi , Pinar Ozturk , Kwok-wing Chau This is my paper · ORCID

classification cs.LGstat.ML

keywords floodpredictionmachinelearningliteraturereviewhybridmodelsensemblesystemswaveletneuralnetworksANFISsupportvectormachines

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a systematic literature review of machine-learning (ML) methods for flood prediction, screening 6,596 articles and selecting 180 comparative studies. It tries to establish which ML methods are most promising for short-term and long-term flood prediction, and which improvement strategies actually work. The central claim is that hybrid and ensemble methods—particularly ANFIS, wavelet neural networks, and decomposition-based hybrids—tend to outperform single models, and that hybridization, data decomposition, algorithm ensemble, and model optimization are the four effective strategies driving progress. The paper would matter because flood prediction directly affects evacuation planning, risk reduction, and policy, and a clearer map of which method class works for a given lead time could help hydrologists choose models.

What carries the argument

The central object of the review is a classification-and-comparison taxonomy rather than a new algorithm. Every selected study is sorted by prediction lead time (short-term versus long-term, with one week as the working boundary) and by model architecture (single versus hybrid), then evaluated on reported R² and RMSE values plus qualitative ratings of complexity, ease of use, speed, accuracy, and input dataset. This apparatus lets the authors aggregate performance signals from heterogeneous case studies into comparative tables and figures, from which they read the trend that hybrids, decomposition, ensembles, and optimization are the recurring levers of improvement.

What would settle it

A single controlled benchmark run on one or more public streamflow and rainfall datasets with identical train/test splits, showing that plain ANNs or SVMs match or beat the recommended hybrids such as ANFIS, WNN, or EEMD-ANN on both short and long lead times, would undermine the paper's central comparative conclusion.

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Extended reading notes

Core claim

On its own terms, the paper claims that no single ML method dominates all flood-prediction tasks, but the comparative evidence organized by lead time points to distinct winners. For short-term prediction (lead times up to about a week), ANN variants, SVM/SVR, ANFIS, and decision-tree models are reported as the most promising single methods, while hybrid models such as ANFIS and wavelet-based networks give better accuracy beyond a two-hour lead time. For long-term prediction (weekly to annual), the paper reports that data-decomposition hybrids—WNN, WARM, EEMD-ANN, modified EMD-SVM, and similar—outperform undecomposed approaches, and that ensemble prediction systems reduce uncertainty. The paper further claims that the field's progress is driven by four strategies: hybridizing ML with other ML, soft-computing, or physical models; decomposing input time series; ensembling predictors; and adding optimizer algorithms for architectural or parameter tuning.

Load-bearing premise

The survey's rankings treat RMSE and R² values reported in different studies, catchments, regions, lead times, and data periods as directly comparable, even though those numbers depend heavily on basin scale, flood magnitude, and dataset length.

Editorial extensions

If this is right

Hydrologists building short-term flood warnings should consider ANFIS or wavelet-hybrid ANN/SVR models over plain ANNs, especially for one-to-three-hour lead times.
Longer-lead forecasts, from weekly to annual, are best served by decomposition-based hybrids such as wavelet neural networks, WARM, EEMD-ANN, and modified EMD-SVM.
Ensemble prediction systems built from ANNs, MLP, SVM, or random forests can reduce forecast uncertainty and improve robustness.
Decomposing the input time series before training appears to be a broadly transferable accuracy boost across methods.
Optimization algorithms that tune network architecture and parameters are expected to yield further gains in both short- and long-term flood prediction.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper's rankings treat RMSE and R² values reported in different studies, catchments, lead times, and data periods as comparable; normalizing these metrics by catchment runoff variance could shift the reported ordering of methods.
The four identified strategies are not fully independent: decomposition and ensemble overlap heavily in models like EEMD-ANN, so the effective number of distinct levers may be smaller than four.
A controlled benchmark on a single large hydrometeorological dataset, with identical train/test splits, would be a natural test of whether the recommended hybrids truly beat plain ANNs and SVMs.
For operational flood warning, the review underweights the trade-off between accuracy and lead time: a model with slightly lower R² but several extra hours of warning could be more valuable than the top-ranked method.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Bottom line: this is a useful catalogue and a reasonable organizing scheme, but the central comparative claim does not survive contact with its own figures. The authors pool RMSE and R2 values from studies with different flood variables, catchments, lead times, and periods, then rank methods. Same units do not make RMSE commensurable—a 0.3 m water-level error means different things in different tidal ranges—and R2 depends on the variance of each observed series. So Figures 7–10 and Tables 2 and 5 cannot support the 'most promising methods' conclusion. The stress-test note is right.

What is genuinely useful: the short-term/long-term and single/hybrid split is a sensible way to organize a messy literature; the search procedure is transparent (6596 articles down to 180); the narrative summaries of individual studies are broadly plausible. The qualitative claim that hybridization, decomposition, ensembling, and optimization are common improvement strategies is well supported and probably true. A practitioner entering flood ML would get a decent map of method families and named hybrid systems.

Soft spots beyond the invalid pooling: internal inconsistencies are real—Table 2 references 'accuracy analysis of Figure 3,' which is a publication-count chart, and the two figures labelled 10 appear to be mislabelled in Section 6. Self-citations are heavy, especially to Mosavi's own surveys, though that alone is not disqualifying. The abstract's phrase 'introduces the most promising prediction methods' overstates what a narrative review can establish without a common benchmark.

Who is this for? Someone who wants a broad list of studies and method names, not someone who needs a reliable ranking. With major revision—fix labels, reframe the conclusions as qualitative trends, and either remove the pooled RMSE/R2 ranking or replace it with an explicitly exploratory comparison—it could become a passable survey. As it stands, I would still send it to peer review because the coverage is substantial and the taxonomy deserves scrutiny, but I would expect reviewers to insist that the quantitative ranking be stripped or redone.

Referee Report

4 major / 7 minor

Summary. This paper presents a literature review of machine learning methods for flood prediction. The authors classify the surveyed studies according to prediction lead time (short-term versus long-term) and according to whether the method is single or hybrid. They compile 180 studies and provide narrative summaries of individual applications, supplemented by comparative performance analyses that pool reported RMSE and R2 values across studies. On this basis, the paper claims to identify the most promising prediction methods for short- and long-term floods, and it concludes that hybridization, data decomposition, algorithm ensembling, and model optimization are the most effective strategies for improving ML-based flood prediction.

Significance. If the comparative claims were valid, this review would be a useful synthesis of a fragmented and fast-growing literature. The paper's taxonomy (single versus hybrid, short-term versus long-term) is reasonable, and the compilation of 180 studies, together with the descriptive statistics on publication trends, is a service to the community. The narrative summaries are broadly plausible and the authors are transparent about their inclusion criteria (journal-level quality metrics, comparative content). However, the central quantitative comparison is not methodologically sound: it aggregates RMSE and R2 values from heterogeneous studies without any normalization or meta-analytic correction, so the resulting rankings of methods do not support the paper's headline claims. The paper's value is therefore primarily as a qualitative survey, not as an evidence-based ranking of methods. With a reworked comparison, or with the quantitative ranking removed, the review could be publishable.

major comments (4)

[4.3, Figures 7–10, Tables 2 and 5] The central quantitative comparison that supports the paper's main claim is not methodologically valid. The paper states in Section 4.3 that 'we made sure that the unit of RMSE was the same, and, for the multiple RMSEs, the average was calculated,' but equal units are not sufficient for comparability. RMSE is scale-dependent across flood resource variables (water level in metres, streamflow in m3/s, rainfall in mm) and across catchments of different sizes; R2 depends on the variance of the observed series and on the test period. The studies pooled in Figures 7–10 differ in lead time, region, data period, and evaluation protocol, and no normalization, effect size, or within-study paired comparison is applied. Therefore the averaged RMSE and R2 values, and the resulting rankings in Figures 7–10 and the accuracy ratings in Tables 2 and 5, cannot support the abstract's claim that the paper 'introduces the most promising prediction methods' for short- and long-term floods. The authors should either present the comparison as purely qualitative or conduct a formal meta-analysis with appropriate standardization and a clearly defined, reproducible study-selection protocol.
[3.8, 4.1, Table 1] The paper's own taxonomy is internally inconsistent. Section 3.8 defines long-term prediction as lead time greater than one week, yet Table 1, which is labelled 'Short-term predictions using single machine learning methods,' includes the row 'MLP vs. Kohonen NN [154] Flood frequency analysis Long-term China.' This directly contradicts the stated definition and indicates a classification error. Because the division into short-term and long-term is the organizing principle of the entire survey, this inconsistency must be resolved by correcting the table row or revising the definition.
[6, Figures 9–10] Figure 10 is captioned 'Comparative performance analysis of hybrid methods of ML for short-term prediction,' and the text in Section 6 repeats that 'Figure 10 represents the comparative performance analysis of hybrid methods of ML for short-term prediction,' even though the surrounding paragraph is discussing long-term prediction and Figure 9 is described as covering single methods for long-term prediction. One of the two (text or figure label) is wrong, and the error directly affects the interpretation of the long-term hybrid results, which are central to the paper's conclusions. Please correct the mislabelling and verify that the figure contents match the intended lead-time category.
[4.1, 4.2, 5.1, 5.2; Tables 2 and 5] The qualitative ratings in Tables 2 and 5 (e.g., 'Fair', 'High', 'Fairly high') are presented as comparative analyses, but the method for assigning these ratings is not described. The text states that the tables were created 'based on the revisions that were made on the articles of Table 1 and also the accuracy analysis of Figure 3,' yet Figure 3 is a chart of the number of articles per method, not an accuracy analysis. This internal inconsistency and the lack of a reproducible rating protocol make the rankings in these tables unsupported. The authors should specify the rubric used, or remove the ratings and rely on the narrative discussion.

minor comments (7)

[Abstract and Section 1] The sentence 'To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions' is a run-on and should be split or rewritten for clarity.
[3.8] The sentence 'Furthermore, if the prediction leading time to flood is three days longer than the confluence time, the prediction is considered to be long-term [37,58]' is unclear; 'confluence time' is not defined, and the threshold seems to conflict with the later definition of 'greater than a week.'
[4.3] The statement 'generally R2 > 0.8 is considered as an acceptable prediction' is a heuristic that needs a citation or a more nuanced discussion, since acceptable R2 values depend on the variable and context.
[5.2] The sentence 'References [224,226] compared the performances of ANFIS, ANNs, and SVM for the monthly prediction of floods' appears to cite the wrong references: [224] is a review of a multiobjective optimization package and [226] is about solar radiation prediction, not flood prediction. Please correct the citations.
[Section 5 (Conclusions) numbering] The conclusions section is numbered '5' but appears after Section 6; the numbering should be sequential (for example, Section 7).
[Figures 3 and 4 captions] 'Reference year: 2008 (source: Scopus)' is ambiguous; the figures appear to plot data from 2008 to 2017, so the caption should say 'Data source: Scopus, 2008–2017' or similar.
[General] The manuscript contains numerous typos and grammatical errors (for example, 'reduc tion', 'minimiz ation', 'the results of [149] provides similar conclusions', and 'SVM was demonstrated as a potential candidate'), and a careful language edit is needed.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the paper is a literature synthesis, and its comparative claims rest on collected external studies rather than on self-referential construction.

full rationale

This manuscript is a literature review, not a derivation, so the equation-level circularity patterns do not apply. The central claims—that certain ML methods are promising and that hybridization, decomposition, ensembling, and optimization are effective strategies—are presented as summaries of 180 externally published comparative studies. The paper does not fit a parameter and then predict a closely related quantity; it tabulates reported RMSE and R2 values and aggregates them. That aggregation is methodologically fragile because RMSE is scale-dependent and R2 depends on the variance of each test series, but this is a validity limitation, not circularity: the summaries are not equivalent to the inputs by construction. The authors do cite their own prior work in several places, including references [38,45,49,52,55,56,176,195,221,222,226], and the first author appears in the funding and acknowledgment statements, but none of these self-citations is invoked as a forcing theorem or as the sole justification for the review's classification or conclusions. The taxonomy of single vs. hybrid methods is a descriptive organizing device, not a result derived from the papers it classifies. The statement in Section 4.3 that 'we made sure that the unit of RMSE was the same, and, for the multiple RMSEs, the average was calculated' is an attempt at fairness, and while equal units do not make cross-study RMSE values commensurable, this is a statistical comparability problem rather than a self-definitional or fitted-input circularity. No passage asserts a limitation that would indicate a circular step, and no load-bearing claim reduces to its own inputs. The appropriate finding is therefore no significant circularity, with a score of 1 reflecting only the presence of non-load-bearing self-citations.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The review leans on several unstated assumptions about the comparability of reported metrics and the validity of journal-level quality filters. These assumptions are load-bearing for the comparative rankings, but they are not tested or justified in the paper.

assumptions (3)

domain assumption Reported RMSE and R2 values in the reviewed studies are comparable across different catchments, regions, and lead times, so averaging and ranking them is informative.
This supports the comparative analyses in Section 4.3 and Figures 7-10, which average RMSE and R2 values from many studies without normalizing for dataset characteristics.
domain assumption Journal-level metrics (SNIP, CiteScore, SJR, h-index) are valid proxies for individual paper quality, and excluding papers below some threshold does not bias the survey.
Used in Section 2 to refine the 6,596 search results down to 180 papers; the validity of this filtering is not tested.
domain assumption The paper's operational definition of long-term (lead time greater than a week) aligns with the definitions used in the underlying source papers.
The review imposes this definition in Section 3, but source papers may use different thresholds (monthly, seasonal, annual), making the taxonomy potentially inconsistent.

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Cite this review

Pith. "Pith review of Flood Prediction Using Machine Learning Models: Literature Review." pith.science (2026). https://pith.science/paper/UX3FYKHE

@misc{pith2026190802781,
  author       = {Pith},
  title        = {Pith review of: Flood Prediction Using Machine Learning Models: Literature Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UX3FYKHE}},
  note         = {Machine review of arXiv:1908.02781}
}

read the original abstract

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods.

Figures

Figures reproduced from arXiv: 1908.02781 by the authors.

**Figure 1.** Flowchart of the search queries. Section 3 presents the state of the art of ML in flood prediction. A technical description on the ML method and a brief background in flood applications are provided. Section 4 presents the survey of ML methods used for short-term flood prediction. Section 5 presents the survey of ML methods used for long-term flood prediction. Section 6 presents the conclusions. 3. State of the Art … view at source ↗

**Figure 3.** Major ML methods used for flood prediction in the literature. Reference year: 2008 (source: Scopus). Considering the ML methods for application to floods, it is apparent that ANNs, SVMs, MLPs, DTs, ANFIS, WNNs, and EPSs are the most popular. These ML methods can be categorized as single and hybrid methods. In addition to the fundamental hybrid ML methods, i.e., ANFIS, WNNs, and basic EPSs, several different research… view at source ↗

**Figure 4.** The progress of single vs. hybrid ML methods for flood prediction in the literature. Reference year: 2008 (source: Scopus). Furthermore, the types of prediction are often studied with different lead-time predictions due to the flood. Real-time, hourly, daily, weekly, monthly, seasonal, annual, short-term, and long-term are the terms most often used in the literature. Real-time prediction is concerned with anywhere b… view at source ↗

Figures from the paper (4 more)

**Figure 5.** Figure 5: Taxonomy of the survey—ML methods for flood prediction. ML for flood prediction Long-term Short-term Hybrid methods Single methods Hybrid methods Single methods [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]

**Figure 6.** Figure 6: Taxonomy of the survey. Step 1 involved running the queries one by one; step 2 involved checking the results of the search, and initiating the next search; step 3 involved identifying the comparative studies on ML models of prediction, refining the results and building…

**Figure 7.** Figure 7: Comparative performance analysis of single methods of ML for short-term flood prediction using R2 and root-mean-square error (RMSE). 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 Comparative performance analysis of single methods for short-term flood prediction RMSE R2 …

**Figure 9.** Figure 9: Comparative performance analysis of single methods of ML for long-term prediction. Either in short-term [227] or long-term rainfall–runoff modeling [50], overall, the accuracy, precision, and performance of most decomposed ML algorithms (e.g., WNN) were reported as bet…

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