Flood Risk Mitigation Research Articles

Cost benefit analysis of flood risk in the area of Thrace in Greece

Cost-benefit analysis can significantly aid the decision-making process for flood risk reduction and mitigation. Decision-makers can use it to weigh the expenses of various flood mitigation strategies against their possible upside in terms of lowering the risk of losses and damage. Decisions regarding how to allocate resources for flood risk management can be made more intelligently with the use of this information by policymakers and stakeholders. During severe rainfall or snowfall, these rivers are prone to flooding, which can harm structures such as homes, businesses, and infrastructure, have negative impacts to land uses and as a result they can cause high opportunity costs. Flooding in the Thrace region can have indirect and direct expenses, including property damage and lost commercial activity. These costs can include communication breakdowns and higher insurance rates. The predicted total economic losses from erosion for all of Greece are €356 million and €649 million for a 0.5- and 1-meter sea level increase, respectively.

Assessment of socio-economic strategies for managing regional flood risk in an urban coastal catchment

With the cascading flooding events over the past decades, several structural and non-structural measures have been effectively implemented for improved flood management. However, the uneven impacts of the flooding events have highlighted that socio-economic inequality amongst the community needs to be addressed along with hazards to devise long term and sustainable flood management strategies. This study aims to evaluate the impact of socio-economic adaptive scenarios over an urban coastal catchment for reduction in flood risk. Flood risk is estimated utilizing socio-economic aspects (vulnerability) and flood inundation (hazard), for the highly flood-prone coastal city of Mumbai, India. A multivariate approach is used for assessing socio-economic vulnerability, starting with Principal Component Analysis followed by Data Envelopment Analysis, for various socio-economic scenarios, i.e., by reducing percentage of certain feasible socio-economic sensitivity indicators, to identify the key indicators, which contribute to significant change in vulnerability. The flood hazard is assessed through a comprehensive hydrodynamic flood modelling approach to quantify the flood inundation depth and extent. The flood risk is, thereafter, evaluated by combining socio-economic vulnerability and flood hazard using bivariate risk mapping approach. The results indicate that, with the decrease in alteration percentage of certain feasible socio-economic sensitivity indicators, the overall vulnerability and flood risk may be reduced. The areas under very high and high vulnerability classes reduce gradually for all scenarios as the percentage reduction for various indicators is varied from 5 % to 15 %, total illiteracy being the most significant indicator. This study marks the first effort to discern socio-economic vulnerability indicators that have the potential to significantly diminish flood risk, thereby constituting a viable long-term flood risk mitigation strategy. This novel framework is generic and applicable to any flood prone catchments, to devise appropriate long-term flood management measures.

The performance of two urban flood regulation models using different input data

Increasing climate change has led to an increase in urban flood events. Events with a return period of twenty years become events with a period of two to three years. The primary objectives of this study are to evaluate the performance of flood regulation models using different input data and compare their performance for flood regulation supply (FRS) assessment. The assessment of the ecosystem services of urban FRS was performed using two models: Urban Flood Risk Mitigation (UFRM) by Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and IMECOGIP Waterflow Regulation (Retention Model) (WRRM). They rely on relatively accessible open-source data, require a short time to model each scenario and provide opportunities to interpret results in different spatial and measurement units. We performed several improvements to the input data, including updating the national ecosystem-type assessment for the case study area by connecting it with the Urban Atlas typology and proposing an approach for soil data refinement. The outputs of the two models show significant differences in FRS values for each land-use/land-cover (LULC) class. The UFRM assesses different impervious urban classes with different values, varying from very low to medium. In contrast, the WRRM assesses the FRS in densely sealed areas with one fixed value for low supply (where nearly all rainfall is transformed into runoff).

A Study on the Analysis of Reduction Effects and Comparison of Economic Feasibility by Applying the LID Technique

This study conducted a comparative review of the effects and project costs of four different flood runoff reduction measures applied in the Dongnam District 1 watershed. Using the XP-SWMM model, the effects of three LID (low-impact development) technologies—infiltration trenches, permeable pavement, and green roofs—were analyzed. The results showed reductions in both peak flood discharge (32.65 m<sup>3</sup>/s → 27.85 m<sup>3</sup>/s) and total runoff volume (99,725 m<sup>3</sup> → 86,814 m<sup>3</sup>). However, the application of LID technologies alone is insufficient for mitigating flood risks. Therefore, flood reduction measures were categorized into four CASEs, combining LID technologies with conventional stormwater management facilities. The cost comparison for each CASE was as follows: CASE 1 (LID + pump) at 22.3 billion KRW, CASE 2 (LID + stormwater storage facility) at 20.3 billion KRW, CASE 3 (pump) at 20.6 billion KRW, and CASE 4 (stormwater storage facility) at 21.0 billion KRW. Despite the use of LID technologies, the project costs for CASEs 3 and 4, which utilized existing reduction methods, were higher. This indicates that integrating LID technologies with conventional reduction measures is likely the most effective approach.

1D-2D hydrodynamic and sediment transport modelling using MIKE models

A comprehensive modeling framework utilizing hydrodynamic and sediment transport models (MIKE Hydro River, MIKE 21 FM, MIKE 21C) was applied to the 33.57 km stretch of the Banshadhara River from Banshadhara bridge, Gunupur to Kashinagar town. Daily discharge, water levels, and sediment data from 2013 to 2022 of Gunupur and Kashinagar gauge stations was used as model input, and calibration–validation of models. Additionally, satellite remote sensing data used for determined Manning’s roughness (n) values as well compared the model outputs. Calibration procedures ensured high accuracy, with MIKE Hydro River achieving 98.3% agreement between observed and simulated water levels. 1D sediment transport model showing significant variability in total bed load values ranging from 0.3 to 3.83 m^3/s. The Wilcock & Crowe gravel bed load formula accurately predicted bed load transport within the expected magnitude, albeit tending to overestimate transport, indicative of supply-limited conditions. 2D MIKE 21 FM simulations highlighted flow dynamics, with rapid flood arrival at Kashinagar and varying current speeds along the river, especially at bends. MIKE 21C, focused on the central portion, indicated erosion processes at river bends with high discharge. Sediment transport model accuracy was approximately 86.7%, capturing general trends despite occasional discrepancies. Over a 10-year simulation, erosion trends and potential river course shifting were observed, particularly near bends. The analysis provides valuable insights for river management, flood risk mitigation, water resource management, infrastructure planning, and environmental preservation in the Banshadhara River basin and similar environments.

IMPLEMENTING FLOOD-RESISTANT DESIGN STRATEGIES IN COASTAL REGIONS

This systematic review synthesizes findings from 85 articles to examine flood-resistant design strategies in coastal regions, emphasizing an integrated approach that combines traditional engineering solutions, nature-based strategies, green infrastructure, adaptive design, and predictive modeling. The review identifies a continuing reliance on engineered defenses, such as seawalls and automated floodgates, which have proven effective but are limited in addressing the escalating impact of climate change. Findings highlight the growing importance of nature-based solutions—wetlands, mangroves, and coral reefs—that not only mitigate flood risks but also enhance biodiversity and long-term sustainability. Green infrastructure, including permeable pavements and rain gardens, emerges as a valuable urban solution, although it faces implementation challenges in densely populated areas. The integration of predictive modeling and real-time data is transforming flood management by enabling proactive and adaptive responses, though these technologies remain financially inaccessible for many regions. Comparative analysis underscores that flood-resilient design is highly context-dependent, with successful practices varying based on local geography, resources, and socio-economic factors. This review concludes that future flood-resilient infrastructure must prioritize adaptable, sustainable, and region-specific solutions, highlighting the need for cross-disciplinary collaboration and continued policy support to enhance flood resilience and protect vulnerable coastal communities against the effects of climate change.

Mitigating flood risk at Shah Alam, Malaysia for sustainable development

The natural environment and infrastructure are severely damaged by floods, which are becoming frequent occurrences. It may not be able to stop floods given the impending climate change and variability of the weather. Yet mapping flood risk can help with both mitigation and prevention of flooding. The Shah Alam experiences flash floods yearly, which is why it was chosen. An interactive approach to decision-making under multi-criteria decision analysis (MCDA), the analytical hierarchy process (AHP), is used. The flood risk zone is divided into five categories very low-risk (32.38%), low-risk (22.25%), moderate-risk (23.17%), high-risk (18.87%), and very high-risk (3.33%) respectively. Based on the data gathered for this study, the economic losses are identified, low-risk areas (RM 2091), moderate-risk areas (RM 2343), and high-risk areas (RM 2728). The historical flood events occur in the Seksyen 36, 18, 17, and most of the southern part of Shah Alam, Malaysia. Rainfall and the Klang River have the biggest effects on these regions, with water drainage systems being critical in the Shah Alam area to reduce the risk of floods. When analysing flood risks in the Shah Alam region and throughout the nation, important stakeholders both state and non-state actors can benefit greatly from the flood susceptibility map provided by this study.

Determining an optimal run-off coefficient method for estimating peak discharge in the Lesti River catchment

The run-off coefficients provide vital hydrological data used for river discharge forecasts and flood risk management. Selecting an appropriate method to determine this coefficient is essential for accurately estimating peak discharge. This study compared the effectiveness of the Hassing, Cook, and U.S. Forest Service methods integrating GIS in estimating run-off coefficients in the Lesti River catchment area from 2013 to 2019. The findings revealed that the run-off coefficient was determined to be 0.188–0.243 using the U.S. Forest Service method, 0.194–0.213 using the Hassing method, and 0.466–0.480 using the Cook method. These results showed a rapid increase in the run-off coefficient within the Lesti River catchment area, signifying a heightened susceptibility to flooding. This is particularly concerning as the Lesti River is a primary tributary to the Brantas River. The comparison of estimated versus observed peak discharge emphasised the superiority of the runoff coefficient associated with the Hassing method over alternative methodologies when utilised as input data for peak discharge estimation. This was evident by the notable measurement error values of 11% for MAPE and 0.58 for MAE. The Hassing method emerged as the most appropriate and reliable for reflecting run-off characteristics in the Lesti River catchment area. Additionally, it proved to be the most accurate for estimating run-off coefficients in the Nakayasu process for peak discharge estimation. Consequently, applying the Hassing method offers a viable strategy for effectively mitigating flood risks in the Lesti catchment area.

Multi-Approaches for Flash Flooding Hazard Assessment of Rabigh Area, Makkah Province, Saudi Arabia: Insights from Geospatial Analysis

Flash flood hazard assessment is a critical component of disaster risk management, particularly in regions vulnerable to extreme rainfall and climatic events. This study focuses on evaluating the flash flood susceptibility of the Rabigh area, located along the Red Sea coast in Makkah province, Saudi Arabia. Using advanced GIS tools and a spatial multi-criteria analysis approach, the research integrates a variety of datasets, including remotely sensed satellite data, the SRTM Digital Elevation Model (DEM), and topographic indices. The main goal was to produce detailed flood susceptibility maps based on the morphometric characteristics of the region’s drainage basins. These basins were delineated and assessed for their flood vulnerability using three distinct modeling techniques, each highlighting different aspects of flood behavior. The results show that the northern basin (Dulaidila) and the central basins (Rabigh, Algud, and Al Nuaibeaa) exhibit the highest flood risk, with significant susceptibility also observed in the southern basins (Ofoq and Saabar). Other basins in the region display moderate susceptibility levels. A key aspect of this analysis was the overlay of the integrated flood susceptibility map with the Topographic Position Index (TPI), a crucial topographic indicator, which helped refine the understanding of flood-prone areas by linking basin morphometry with in-situ topographic features. This study’s comprehensive approach offers valuable insights that can be applied to other coastal regions where hydrological and climatic data are scarce, contributing to more effective flood risk mitigation and strategic planning.

Urban Green Infrastructure and Ecosystem Service Supply: A Study Concerning the Functional Urban Area of Cagliari, Italy

Urban green infrastructure (UGI) is a network composed of natural and semi-natural areas, such as greenspaces, open areas, and water bodies, designed to enhance the provision of ecosystem services and to meet the needs and expectations of local communities. UGIs should be accessible and should improve the well-being and health of their users, protect and enhance biodiversity, and allow for the enjoyment of natural resources. The study proposes a methodological approach to defining a UGI, conceived as a network of areas connected by urban ecological corridors and suitable for providing climate regulation, flood risk mitigation, outdoor recreation, and biodiversity and habitat quality enhancement. The methodology is applied to the functional urban area (FUA) of the City of Cagliari, Italy. The analysis results show that areas with high values of climate regulation, carbon storage and sequestration, and habitat quality enhancement are particularly suitable to be part of a UGI. Although values for outdoor recreation appear to be less significant, the provision of this service is particularly relevant within the Cagliari FUA. However, areas characterized by high values of flood risk mitigation show a different behavior, which highlights how the presence of impermeable surface within urban areas is associated with a loss of patch connectivity.

The interplay between the urban development of Rome (Italy) and the Tiber River floods: A review of two millennia of socio-hydrological history.

The urban development of Rome (Italy) has been intertwined with the dynamics of the Tiber River since its foundation. In this review paper, we analyse more than 2500years of flood history and urban development to untangle the dynamics of flood risk and assess the resulting socio-hydrological phenomena. Until the 1800s, urban dwellers living in the riparian areas of the Tiber River were accustomed to frequent flooding. From the 1900s, the construction of flood walls reshaped the co-evolution of hydrological, economic, political, technological, and social processes. As a result, while the probability of flooding is currently very low, its potential adverse consequences would be catastrophic. From the analysis of the long-term feedback between urban development of Rome and flood events from ancient times to present days, it emerges the crucial need for an effective flood risk mitigation strategy that combines structural and non-structural measures. In particular, heightened flood risk awareness and preparedness to cope with rare but potentially devastating events is key to alleviate flood risk.

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R2 values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R2 values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.

Towards integrated multi-risk reduction strategies: A catalog of flood and earthquake risk mitigation measures at the building and neighborhood scales

The integration of risk reduction measures for earthquakes and floods is a promising concept in the field of multi-hazard disaster risk reduction. However, existing studies mostly discuss it conceptually, often missing a focus on decision-making. This paper introduces a novel approach by examining synergies and trade-offs between earthquake and flood risk reduction measures. The paper first conducts a detailed analysis of selected measures for these two hazards. It then explores how these individual risk mitigation measures can be strategically integrated at the building level to maximize synergies, potentially resulting in time and cost savings. Furthermore, it discusses the limitations of current decision-making procedures for single-hazard risk mitigation selection when applied to multi-hazard risk (MHR) scenarios. To address these challenges, the study proposes new decision variables to be incorporated into existing Multi-Criteria Decision-Making frameworks with the aim of facilitating strategic risk reduction prioritization for MHR scenarios, ensuring sustainability and community resilience. To aid implementation (i.e., the phase in which the measure would be selected and tested in a series of case study buildings), a catalog of analyzed risk reduction measures is developed and provided as Supplementary Material. In future developments, this catalog will facilitate the integration of these measures into decision-support tools for evidence-based multi-hazard disaster risk reduction.

Interregional watershed services: Valuation of the benefits of controlled embankment breaches to manage urban flood risk

The Lower Mekong delta of Vietnam, particularly Can Tho city, is highly susceptible to flooding which urgently calls for significant efforts to mitigate urban flood risks. A possible measure to reduce flood risk is the application of controlled embankment breaches (CEB) in a rural province upstream. Utilising a Protection Motivation Theory approach, this paper uses a contingent valuation to assess the welfare effects of a reduction of flood risks in the city resulting from CEB in the upstream region of the delta. The findings revealed that households residing in the downstream areas of Vietnamese Mekong Delta were willing to financially support the implementation of the CEB upstream in An Giang city, as a measure to alleviate urban flood threats in Can Tho city. These results suggest that interregional cooperation, involving the concept of payments for watershed services between regional jurisdictions, may help to effectively mitigate flood risk in a low-lying population centre in a major river delta. As such, these findings can inform policies for urban flood risk mitigation regarding the development of a more comprehensive flood risk reduction strategy in similar geographical contexts.

Dams, dams and more dams: Issues in evaluating business cases for dam expansion in Australia

Abstract Conventional interventions for mitigating flood risk and increasing water security often focus on large-scale engineering solutions despite evidence of significant cost overruns and negative environmental impacts. This study is an example of participatory research in water economics, motivated (and requested) by concerns from downstream communities and Indigenous stakeholders that impacts of a proposed dam upgrade and storage increase were not adequately considered. We reviewed the preliminary benefit-cost analysis (BCA) of the proposed AUD$2.1 billion investment to expand the Wyangala Dam in Australia and conducted a new BCA incorporating downstream community perspectives. The original economic analysis favoured the proposed investment, but it underestimated costs and overestimated benefits. Our economic analysis was complemented with a qualitative analysis of downstream community perspectives, plus a quantitative analysis indicating that project costs were underestimated by a minimum of 116%. In comparison, benefits were overestimated by 56%. Neglecting the potential impact of climate change also severely overestimated the original benefit-cost ratio. Based on our calculations, expanding the dam was unlikely to yield a net social benefit. In 2023, a new government decided the dam expansion was too expensive. We recommend policymakers prioritise independent evaluations and community engagement for BCAs on large-scale water infrastructure projects to ensure equitable investment decisions that maximise social welfare and adequate environmental assessment.

Flood risk awareness and transportation mode choice in communities with varying flooding frequencies: The cases of Bozkurt, Kastamonu, Turkey and Malanday, Marikina, Philippines

This study investigates the post-flood transportation mode choices (TMC) of communities exposed to floods of varying frequencies, focusing on Bozkurt, Turkey, recently afflicted by a major flood, and Malanday, Philippines, which endures an average of 10 floods annually. The study considers 17 factors affecting TMC, including age, occupation, income, housing type, flood experience, education level, changes in travel time, primary transportation mode, duration of residence, gender, marital status, relocation status, personal losses, disruption of daily activities, time to return to routine, satisfaction with road maintenance, and satisfaction with flood measures. Data were collected through surveys and analysed using regression analysis.The findings indicate that personal and cultural characteristics, and flood frequency, have limited effects on TMC. The most impactful factors on post-flood TMC are changes in travel time (42.11 %), personal losses (23.75 %), and satisfaction with road maintenance (11.30 %). The study demonstrates that flood risk awareness is directly related to education level and flood experience. In Malanday, where 84 % of the population are university or high school graduates, floods are regarded as routine events. In contrast, in Bozkurt, where only 37 % have similar education levels, the community experienced panic under flood conditions, adversely affecting decision-making processes.The research provides insights into how communities exposed to floods adjust their TMC, offering essential information for mitigating flood risks and increasing flood awareness. Moreover, the findings deliver suggestive information for local governments and planners in developing more effective flood management and transportation planning strategies.

Investigating Flow around Submerged I, L and T Head Groynes in Gravel Bed

Riverbank erosion poses a significant threat to the stability and integrity of river training structures. River training structures such as groynes are important components of sustainable development as they play a crucial role in mitigating flood risks, controlling erosion, and supporting the habitat for aquatic organisms. The habitats vary largely according to the groyne type. A comprehensive comparative analysis of the flow field around the I, L, and T head groynes in the gravel bed is drawn. This study will be of immense use for riverbank protection in hilly terrain where streams are mostly dominated by the gravel bed. Laboratory experiments were conducted in a channel with a sediment bed as gravel of size 9.36 mm. Consistent flow conditions were maintained, with a flow depth (D) of 0.136 m and Froude no (Fr) of 0.61. The performance of these groynes, quantified using Lp (length of bank protection), was investigated. LHG and THG, notably, instigate more profound scour depths, recording values of 0.295 D and 0.29 D, respectively, while IHG trails with the value of 0.21 D. The complex flow field involving velocity peaks, decelerated, and negative flow is discussed and is attributed to flow separation at the groyne tip and the horseshoe vortex. The Lp for each groyne was estimated, with the IHG providing the maximum bank protection of 1.2 L1, L1 being the transverse length of the groyne. The cost–benefit analysis revealed IHG as the most cost-effective structure. These findings contribute to optimization of riverbank stabilization efforts, enhancing the resilience of hydraulic infrastructure and ensuring the safety and wellbeing of affected communities and ecosystems. The results also provide valuable insight into bank protection by various groynes and highlight their contribution to enhancing the resilience of river systems.

Evaluation of Pedotransfer Functions to Estimate Soil Water Retention Curve: A Conceptual Review

The soil water retention curve (SWRC) is a vital soil property used to evaluate the soil’s water holding capacity, a critical factor in various applications such as determining soil water availability for plants, soil conservation and management, climate change adaptation, and mitigation of flood risks. Estimating SWRC directly in the field and laboratory is a time-consuming and laborious process and requires numerous instruments and measurements at a specific location. In this context, various estimation approaches have been developed, including pedotransfer functions (PTFs), over the past three decades to estimate soil water retention and its associated properties. Despite the efficiencies, PTFs and semi-physical approach-based models often have several limitations, particularly in the dry range of the SWRC. PTFs-based modeling has become a key research topic due to readily available soil data and cost-effective methods for deriving essential soil parameters, which enable more efficient decision-making in sustainable land-use management. Therefore, advancement and adjustment are necessary for reliable estimations of the SWRC from readily available data. This article reviews the evaluation of the current and past PTFs for estimating the SWRC. This study aims to evaluate PTF techniques and semi-physical approaches based on soil texture, bulk density, porosity, and other related factors. Additionally, it also assesses the performance and limitations of various common semi-physical models proposed and developed by Arya and Paris, Haverkamp and Parlange, the Modified Kovács model by Aubertin et al., Chang and Cheng, Meskini-Vishkaee et al., Vidler et al., and Zhai et al. This assessment will be effective for researchers in this field and provide valuable insight into the importance of new PTFs for modeling SWRC.

A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.

Enhancing flood risk mitigation by advanced data-driven approach

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50–70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.