Geospatial approach to pluvial flood-risk and vulnerability assessment in Sunyani Municipality.

Historically, and in recent times, efforts have been to understand, predict, analyze, and quantify floods and their impacts in various countries of the globe. Although recent scientific advances have introduced approaches to assessing the risks presented by flooding, little studies have been carried out in the Sunyani Municipality of Ghana for generating a pluvial flood-risk and vulnerability map for risk identification, resilience, emergency preparedness, and urban spatial planning. In this study, five parameters that influence both pluvial and fluvial flooding were assessed to map flood-prone areas within the Sunyani Municipality. These are precipitation, drainage density, LULC, elevation, and slope, which were integrated in GIS. Using an AHP, weights were assigned to each parameter based on its level of influence on flooding. The findings reveal that 21.32 % of the Sunyani Municipality lies within a highly flood-prone area, 39.65 % in a flood-prone area, while 28.06 % and 10.97 % in slightly flood-prone and not flood-prone areas respectively. Built-up areas close to watersheds with lower elevations and larger drainage density are the places that are highly flood-prone. Some towns within the highly flood-prone and flood-prone areas are Abesim, Newtown, Nkwarbeng, Baakoniaba, Kootokrom, and Penkwase. Highly valued infrastructure such as schools, churches, and hospitals have also been found within these highly flood-prone areas. These findings can aid the government and relevant stakeholders in disaster risk management to be better informed, and to effectively plan and prevent flood challenges in the Sunyani Municipality. Moreover, urban spatial planners in the study setting can consider incorporating the flood hazard maps generated from this study into their spatial plans for proactive physical developments.

Recurrent Flooding and Household Food Access in Central Java, Indonesia.

It is unknown how recurring flooding impacts household diet in Central Java. We aimed to assess how recurrent flooding influenced household food access over 22 years in Central Java by linking the Global Surface Water dataset (GSW) to the Indonesian Family Life Survey. We examined linear and nonlinear relationships and joint effects with indicators of adaptive capacity. We measured recurrent flooding as the fraction of district raster cells with episodic flooding from 1984-2015 using GSW. Food access outcomes were household food expenditure share (FES) and dietary diversity score (DDS). We fit generalized linear mixed models and random forest regression models. We detected joint effects with flooding and adaptive capacity. Wealth and access to credit were associated with improved FES and DDS. The effect of wealth on FES was stronger in households in more flood-affected districts, while access to credit was associated with reduced odds of DDS in more flood-affected districts. Flooding had more predictive importance for FES than for DDS. Access to credit, a factor that ordinarily improves food access, may not be effective in flood-prone areas. Wealthier households may be better able to adapt in terms of food access. Future research should incorporate land use data to understand how different locales are affected and further understand the complexity of these relationships.

Exploring the Association Between Floods and Diarrhea among Under-five Children in Rural India.

OBJECTIVE: Flood is one of the major public health concerns increasing the risk of childhood diarrhea. This study aims to explore the association of floods with diarrhea among under-five children in rural India. METHODS: A cross-sectional study was carried out using large-scale nationally representative data from the National Family Health Survey-5. The Central Water Commission reports between the years 2018 and 2020 were used to group all the districts as non-flood-affected districts or flood-affected districts. Bivariate and multivariate logistic regression models were employed to assess the association of floods with childhood diarrhea. RESULTS: The prevalence of diarrhea was higher among children exposed to three consecutive floods during the year 2019-21 than those children not exposed to flood. Children exposed to flood three times between the year 2018-19 to 2020-21 were associated with a 34% higher likelihood of developing diarrhea than those children exposed to flood one or two times. CONCLUSIONS: Our study suggests that community health workers should target mothers belonging to the poor wealth quintile, young mothers, and mothers with young infants and more children to receive child health related counseling in flood-prone areas.

The need to reconfigure consistency and variability to best manage changing flood risks in Aotearoa-New Zealand.

Flooding is Aotearoa-New Zealand's most frequent natural hazard, and there is high confidence that climate change is making extreme rainfall events more frequent and intense. Additionally, there are significant development pressures which could both increase the number of people and assets at risk and the flood hazard. To date, there is no publicly available consistent approach to accurately determine flood risk on a national scale, nor for how this may be changing; although there is a growing legislative requirement to provide quality information over multiple spatial scales. This paper draws on empirical data to gain insights on how to best manage changing flood risks in Aotearoa-New Zealand from the perspective of centrally organised entities. Findings confirm the need for a nationally consistent approach to flood risk management, better understanding of Aotearoa's communities and their vulnerability to floods, equitable access to quality information and decision-support tools, and better understanding of the economic impacts on differing communities, regions and places. The paper concludes that to achieve a flood-resilient Aotearoa, flood governance needs to be reconfigured to achieve national consistency in flood risk management whilst enabling targeted variability at the local scale.

Timing is (almost) everything: Real options, extreme value theory, climate adaptation, and flood risk management.

Flood risk has become a major concern in many regions due to socio-economic growth and rising water levels. In this paper, we introduce a real options model that integrates the Generalized Additive Models for Location, Scale, and Shape framework with Extreme Value Theory to evaluate adaptation measures for flood risk management. Our model allows for uncertain water level rise, climate indices and growing loss exposure. In a case study of flood risk management for New York City, we find that while immediate investment in a barrier and dike project can provide a substantial net present value ($10.96 billion), investing at the optimal time can significantly improve the investment value by 54.84%. Our sensitivity analysis suggests that discount rate is the most important parameter, followed by the mean level of water rise and the water level rise uncertainty. We also find that investment delay is longer when the discount rate or the water level rise uncertainty is higher or when the expected water level rise is lower.

Under which conditions do extreme events support a paradigm shift? Studying focusing events during two centuries of Swiss flood risk management.

Extreme weather events affect many areas around the world. How a country or region reacts to it can take many forms. In this article, we concentrate on policy responses, as typically found in laws, acts, or strategies. Recent research in climate change adaptation or environmental governance concluded that the degree of severity of extreme events is a crucial indicator that policy action should be taken. The event alone is a necessary, but insufficient condition for policies to be introduced. In this context, we ask: Which conditions must be at stake so that an extreme event is able to deploy its focal power and induce policy introduction or change? To answer this question, we studied more than two centuries of flood risk management in Switzerland. We relied on qualitative and quantitative data, as well as process tracing techniques, to relate event characteristics, media, political, and policy contexts to policy change in flood risk management. Results indicate that two conditions made floods turn into focusing events and support paradigm shift: high economic damage and a policy subsystem's actor constellation favorable to change. We are convinced that our results are also replicable for other natural disasters and other countries than only Switzerland. Supplementary Information: The online version contains supplementary material available at 10.1007/s10113-024-02316-2.

Development of Environmentally Friendly and Economical Flood-Prevention Stones Based on the Sediments of the Yellow River.

The deposition of Yellow River sediment in the middle and lower reaches is a significant factor in the siltation of reservoirs and the occurrence of serious flooding along the river. The efficient and valuable utilization of Yellow River sediment has already become a key research topic in this field. In this study, we have employed Yellow River sediment as the primary material, in conjunction with commercially available slag, fly ash, and quicklime as the binder, to develop a novel type of artificial flood-prevention stone. Following a 28-day standard curing procedure, the highest compressive strength of the prepared artificial stone was recorded at 4.29 MPa, with a value exceeding 0.7 MPa under wet conditions. The results demonstrated that the prepared artificial stone met the specifications for artificial flood-prevention stones. The curing mechanism, as evidenced by analyses from SEM and XRD testing, indicated that the alkali excitation process in the binder, which produced C-A-S-H gel, was the key factor in enhancing the compressive strength of the specimens. Notably, an evaluation of the amount of CO2 emissions and the cost of the artificial stone concluded that the preparation process was both environmentally friendly and cost-effective.

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 2500 years 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.

Projecting urban flood risk through hydrodynamic modeling under shared socioeconomic pathways.

Under future climate change, accurate risk assessment of urban flooding disasters is paramount for effective adaptation and mitigation strategies. However, conventional indicator-based assessment methods often fall short of accurately capturing the complexity of flooding dynamics. Current research predominantly focuses on predicting future hazard shifts while overlooking changes in other critical indicators. In this study, we establish a comprehensive index system for risk assessment, and quantified future changes in most indicators, utilizing the InfoWorks ICM model for hazard simulation and the CLUMondo model for land use predictions. Based on risk assessment results and regional characteristics, we further analyze the key factors driving future risk and discuss corresponding measures. The results indicate an exacerbation of future urban flood risk, with an 18% increase in high risk areas, primarily concentrated in the center of the study area. The dominant indicators are inundation depth and land use over the whole study area. However microtopography significantly affects risk in low-lying areas. Overall, under higher emission scenarios, the influence of GDP and population rises. These findings offer methodological insights for future urban flood risk assessment research and provide policymakers with valuable guidance to develop targeted adaptation measures in response to climate change.

Quantification of geomorphodiversity and its spatial distribution with the flood inundation areas for Assam, India.

Assam, located in the Northeast of India, is highly flood-prone, and the erosional and depositional processes highly influence the landforms. The formation and development of landforms are directly related to the geology, geomorphology, drainage basin characteristics, and soil types of the region. In the present study, a remote sensing and GIS-based geomorphodiversity index (GMI) assessment of Assam is performed using three sub-indices: geodiversity, morphometric diversity, and drainage diversity index. Sixty-six potential geomorphosites are identified with their geological, geomorphological, and GMI classes. With the help of a flood inundation map, the inundated area of each GMI class is calculated. According to the result, 27.02%, 10.76%, and 3.7% of the total area of Assam fall under moderate, high, and very high GMI classes, respectively. Barak Valley and Central Assam region exhibit high to very high GMI values. Geology and geomorphology have a strong influence on GMI values. About 22.32%, 28.33%, 37.18%, 38.25%, and 35.37% of areas with low, moderate, high, and very high GMI are inundated, respectively. This study determined that areas having high GMI can increase the geomorphological heritage value of the region and can play a significant role in promoting geotourism with an increase in the scientific, educational, and aesthetic value of geomorphosites. This study can also help the local governing authorities to conduct and implement better management and conservation policies for vulnerable locations.

Flood risk assessment under the shared socioeconomic pathways: a case of electricity bulk supply points in Greater Accra, Ghana.

This study evaluates flood susceptibility and risk on Bulk Supply Points in the Greater Accra region (GAR) using a Frequency Ratio model based on 15 flood conditioning factors. The model explores the influence of natural, meteorological and anthropogenic factors on flooding occurrences under the Shared Socioeconomic Pathway (SSP) scenarios and assesses flood risks at Bulk Supply Points (BSPs). Flood susceptibility mapping was conducted for both current and future periods under various SSP scenarios. Results reveal that elevation, slope, soil type, distance from urban areas, and SPI are the most influential factors contributing to flooding susceptibility in the region. The current flood map, about 37% of the total area of GAR categorized under the moderate flood-susceptible zone category followed by about 30% categorized under the low flood-vulnerable zone. However, about 16% was categorized under the very high flood-vulnerable zone. The study projects increasing flood susceptibility under the SSP scenarios with intensification under SSP2 and SSP3 scenarios. For instance, the areas categorized as high and very high flood susceptibility zones are projected to expand to approximately 32% and 26% each by 2055 under SSP3. The study also assesses flood risks at Bulk Supply Points (BSPs), highlighting the escalating susceptibility of power assets to flooding under different scenarios. For instance, in the very high scenario, flooding is estimated to reach 640 h in 2045 and exceed 800 h in 2055-more than double the 2020 baseline. The analysis shows the bulk supply points face increasing flood susceptibility, with risks escalating most sharply under the severe climate change SSP3 and SSP5 scenarios. Over 75% of BSPs are expected to fall in the low- to medium-risk categories across SSPs while more than 50% of BSPs are within medium- to high-risk categories in all scenarios except SSP1, reflecting the impact of climate change. SSP3 and SSP5 stand out with over 60% of BSPs facing high or very high flooding risks by 2055. It indicates moderate resilience with proper adaptation but highlights potential disruptions in critical infrastructure, such as BSPs, during persistent flooding. The findings of the study are expected to inform Ghana's contributions towards addressing Sustainable Development Goals (SDGs) 7, 11 and 13 in Ghana.

Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis.

Flooding is a global threat causing significant economic and environmental damage, necessitating a policy response and collaborative strategy. This study assessed global research trends and advances in geospatial and meteorological flood risk assessment (G_MFRA), considering the ongoing debate on flood risk management and adaptation strategies. A total of 1872 original articles were downloaded in BibTex format using the Web of Science (WOS) and Scopus databases to retrieve G_MFRA studies published from 1985 to 2023. The annual growth rate of 15.48% implies that the field of G_MFRA has been increasing over time during the study period. The analysis of global trends in flood risk research and practice highlights the key themes, methodologies, and emerging directions. There exists a notable gap in data and methodologies for flood risk assessment studies between developed and developing countries, particularly in Africa and South America, highlighting the urgency of coordinated research efforts and cohesive policy actions. The challenges identified in the body of extant literature include technical expertise, complex communication networks, and resource constraints associated with the application gaps of the study methodologies. This study advocates for a holistic research approach to flood disaster management through ecosystem-based adaptation that underpins the Sustainable Development Goals to develop innovative flood techniques and models with the potential to influence global decision-making in the G_MFRA domain. Addressing these global challenges requires a networked partnership between the research community, institutions, and countries.

Algorithms to reduce the edge effect and improve the flood histogram quality of a PET detector consisting of two pixelated crystal arrays.

PURPOSE: The performance of detectors is key for a PET scanner to achieve high spatial resolution and high sensitivity. This work aims to develop flood histogram generating algorithms to reduce the edge effect and improve the crystal identification of a PET detector consisting of two optically coupled pixelated scintillator detectors. METHODS: The PET detector consists of two optically coupled detectors, each consisting of a 23×23 LYSO crystal array with a crystal size of 1.0×1.0×20 mm3 read out by an 8×8 SiPM array with a pixel size of 3.0×3.0 mm2. The SiPM array is read out with a resistor network circuit to obtain four position encoding energy signals. A novel center of gravity (COG) positioning algorithm using six signals from the two detectors was proposed and compared to the traditional COG algorithms using either four or eight signals from the detectors. The raised-to-the-power (RTP) method was applied to the three COG algorithms for the PET detector. Different powers of the RTP from 1.0 to 2.5 were evaluated. RESULTS: The proposed COG algorithm significantly improves the crystal identification at the junction of the two detectors as compared to the COG algorithm using four signals of each detector, and improves the crystal identification at the center of the two detectors as compared to the COG algorithm using eight signals from both detectors. The RTP method significantly improves the overall flood histogram qualities of the two COG algorithms using either eight or six signals from the two detectors, and the two COG algorithm provide similar flood histogram quality when a power of 1.5 is used. CONCLUSION: The novel positioning algorithms reduce the edge effect and improve the flood histogram quality for a PET detector consisting of two optically coupled detectors, each consisting of a pixelated scintillator crystal array and a SiPM array with highly multiplexed four signal readout. The positioning algorithms can be used in a PET scanner to improve the spatial resolution and sensitivity.

Socio-hydrological modelling using participatory System Dynamics modelling for enhancing urban flood resilience through Blue-Green Infrastructure.

Cities are complex systems characterised by interdependencies among infrastructural, economic, social, ecological, and human elements. Urban surface water flooding poses a significant challenge due to climate change, population growth, and ageing infrastructure, often resulting in substantial economic losses and social disruption. Traditional hydrological modelling approaches for flood risk management, while providing invaluable support in the analysis of hydrological dynamics of floods, lack an understanding of the complex interplay between hydrological and non-hydrological (i.e., social, environmental, economic) aspects in an urban system, hindering effective flood risk management strategies. In this context, socio-hydrological modelling methods offer a complementary perspective to traditional hydrological models by integrating hydrological and social processes, thereby enhancing the understanding of the complex interactions driving flood resilience. The present work proposes a participatory socio-hydrological modelling approach based on System Dynamics (SD) to quantitatively analyse the interactions and feedback between flood risk and different aspects of the urban system. By combining scientific expertise with stakeholder knowledge, the modelling approach aims to provide decision-makers with a comprehensive understanding of flood dynamics and the effectiveness of resilience-building measures. Furthermore, the role of Blue-Green Infrastructure (BGI) in enhancing urban flood resilience, considering its interplay with grey infrastructure and interactions with various sub-systems, is explored. The results reveal i) the contribution of SD quantitative modelling in supporting the analysis of interactions between flood risk reduction measures and different sub-systems thus offering decision-makers actionable insights into the multifaceted nature of flood risk and resilience; ii) the added value provided by the combination of scientific and stakeholder knowledge in tailoring the model to the case study, quantifying socio-hydrological modelling dynamics limitedly explored in the scientific literature and supporting the selection of measures for increasing flood resilience; iii) the ability of BGI to provide not only hydrological benefits (mainly about the reduction of surface runoff) but also multiple social and environmental benefits (i.e., the co-benefits), especially when coupled with well-functioning grey infrastructure. Reference is made to one of the case studies of the CUSSH and CAMELLIA projects, namely Thamesmead (London, United Kingdom), a formerly inhospitable marshland currently undergoing a process of urban regeneration, with an increasing vulnerability to flooding.

Application of GRACE-based satellite estimates in the assessment of flood potential: A case study of Gangetic-Brahmaputra basin, India.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

[Impact and Mechanism of Medium and Small Flood Regulation in the Three Gorges Reservoir on the Phytoplankton in Tributary Bays].

The regulation of small- and medium-sized floods （RSMF） has become the main mode of regulation in the flood season of the Three Gorges Reservoir （TGR）. To study the response of phytoplankton in the tributary bays of the TGR to the RSMF, a typical eutrophic tributary of the TGR, Xiangxi River, was investigated for the spatiotemporal distribution characteristics of phytoplankton and nutrients in the main and tributary streams from 2020 to 2021. The response characteristics of phytoplankton in the tributary bays to the RSMF were analyzed. The results indicated that during the RSMF, the chlorophyll a （Chl-a） in the water body of the Xiangxi River decreased with the increase in the water level in front of the dam, whereas during the reservoir impounding at the end of flood season, the concentration of Chl-a increased again. During the RSMF, the Chlorophyta and Diatoma were the main communities of planktonic algae in the Xiangxi River. The phytoplankton community changed with the RSMF. When the water level fluctuation increased, diatoms were the main species, whereas when the water level fluctuation was small, blue and green algae were the main species. The concentration of Chl-a was more sensitive to changes in TN concentration. When the flow velocity was >0.25 m·s-1 or the suspended sediment content was >10 mg·L-1, the concentration of Chl-a in the water was inhibited. After 2010, the typical outbreak time of algal blooms in the Xiangxi River Reservoir Bay shifted to the flood season, with only two non-flood season algal blooms. Further attention needs to be paid to the response of algal blooms in the reservoir to small- and medium-sized flood control during the flood season.

Discerning the dynamics of urbanization-climate change-flood risk nexus in densely populated urban mega cities: An appraisal of efficient flood management through spatiotemporal and geostatistical rainfall analysis and hydrodynamic modeling.

While the contribution of climate change towards intensifying urban flood risks is well acknowledged, the role of urbanization is less known. The present study, for the first time in flood management literature, explores whether and how unplanned-cum-urbanization may overshadow the contribution of extreme rainfall to flood impacts in densely populated urban regions. To establish this hypothesis and exemplify our proposed framework, the National Capital Territory (NCT) of Delhi in India, infamous for its concurrent flood episodes is selected. The study categorically explores whether the catastrophic 2023 urban flood could have resulted in a similar degree of urban exposure and damage, had it occurred anytime in the past. A comprehensive spatiotemporal and geo-statistical analysis of rainfall over 11 stations brought about through Innovative trend analysis, Omnidirectional and directional Semi-variogram analysis, and Gini Index indicates a rise in extreme rainfalls. High-resolution land-use maps indicate about 39.53 %, 52.66 %, 56.60 %, and 69.18 % of urban footprints during 1993, 2003, 2013, and 2023, while gradient direction maps indicate a prominent urban surge towards the North-West, West, and Southwest corridors. A closer inspection of the Greenness and Urbanity indices reveals a gradual decline in the green footprints and concurrent escalation in the urban footprints over the decades. A 3-way coupled MIKE+ model was set up to replicate the July 2023 flood event; indicating about 13 % of the area experience "high" and "very-high" flood hazards. By overlaying the flood inundation and hazard maps over land-use maps for 1993, 2003, and 2013, we further establish that a similar flood event would have resulted in lesser damage and building exposure. The study offers a set of flood management options for refurbishing resilience and limiting flood risks. The study delivers critical insights into the existing urban flood management strategies while delving into the urban growth-climate change-flood risk nexus.

Impact of Flood on Breastfeeding Practices at Flood Relief Camps of Pakistan.

Objective: To explore impact of flood on breastfeeding practices and identify barriers in continuation of breastfeeding among mothers residing in flood relief camps. Methods: This exploratory observational study was conducted during visit of medical team of The University of Child Health Sciences, Children's Hospital at flood relief camps of Sindh (7th September to 12th September, 2022) and south-west of Punjab province (18th November to 20th November, 2022). The data was collected on structured questionnaire from 40 lactating mothers residing in flood relief camps. Purposive sampling technique was used in this regard. Results: The mean age of breastfed children was 16.1±7.811 months. There was negative impact on breastfeeding practices (n=21, 52.5%) as frequency decreased in 18(45%) mothers and 3(7.5%) totally stopped breastfeeding. There was significant relation between pre-flood breastfeeding status and impact of flood on breastfeeding practices (p=0.001). The major barriers to appropriate breastfeeding were mother's perception of insufficient breast milk due to inadequate diet (n=6, 15%) or depression and anxiety (n=4, 10%), mother's illness (n=3, 7.5%), constant displacement (n=2, 5%) and provision of breast milk substitutes (n=2, 5%). Conclusion: There has been significant negative impact of flood on breastfeeding practices among lactating mothers residing in flood relief camps. Perception of decreased milk production due to inadequate diet and stress are major barriers in continuation of breastfeeding. Breastfeeding supportive services need to be integral component of flood crisis management.

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.