Flood Risk Areas Research Articles

Production of flood risk maps: Ayancık Stream Example

Natural disasters are events that negatively affect human life and cause material and moral damages. As in the rest of the world, one of the biggest natural disasters after earthquakes in our country is floods. The flood event turns into a disaster in cases caused by human interventions such as the change of the river bed, the increase in the construction on the riversides, and the blocking of the river perpendicular to the flow direction. In this study, risk maps for floods with different return intervals (25, 50, 100 years) were produced with GIS for Ayancık stream in Sinop province. As a result, when the flood risk maps were analyzed, the regions located in the floodplain were considered very risky regions. According to the results obtained, the flood disaster reaches a depth of 7 meters. Ayancık Stream and its surroundings are a very risky area exposed to floods as they have a maximum depth of 3.5 m. In addition, flood risk areas with 50 and 100-year return period floods, increase 3.71 and 12.83 percent, respectively, from 25-year return period flood risk areas. In addition, it was concluded that the majority of the areas affected by the floods in all three turning ranges within the scope of the study area are located in the very risky region, while the extreme risk areas are less than the other risky groups.

Association of environmental and socioeconomic indicators with serious mental illness diagnoses identified from general practitioner practice data in England: A spatial Bayesian modelling study.

BackgroundThe evidence is sparse regarding the associations between serious mental illnesses (SMIs) prevalence and environmental factors in adulthood as well as the geographic distribution and variability of these associations. In this study, we evaluated the association between availability and proximity of green and blue space with SMI prevalence in England as a whole and in its major conurbations (Greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle).Methods and findingsWe carried out a retrospective analysis of routinely collected adult population (≥18 years) data at General Practitioner Practice (GPP) level. We used data from the Quality and Outcomes Framework (QOF) on the prevalence of a diagnosis of SMI (schizophrenia, bipolar affective disorder and other psychoses, and other patients on lithium therapy) at the level of GPP over the financial year April 2014 to March 2018. The number of GPPs included ranged between 7,492 (April 2017 to March 2018) to 7,997 (April 2014 to March 2015) and the number of patients ranged from 56,413,719 (April 2014 to March 2015) to 58,270,354 (April 2017 to March 2018). Data at GPP level were converted to the geographic hierarchy unit Lower Layer Super Output Area (LSOA) level for analysis. LSOAs are a geographic unit for reporting small area statistics and have an average population of around 1,500 people. We employed a Bayesian spatial regression model to explore the association of SMI prevalence in England and its major conurbations (greater London, Birmingham, Liverpool and Manchester, Leeds, and Newcastle) with environmental characteristics (green and blue space, flood risk areas, and air and noise pollution) and socioeconomic characteristics (age, ethnicity, and index of multiple deprivation (IMD)). We incorporated spatial random effects in our modelling to account for variation at multiple scales.Across England, the environmental characteristics associated with higher SMI prevalence at LSOA level were distance to public green space with a lake (prevalence ratio [95% credible interval]): 1.002 [1.001 to 1.003]), annual mean concentration of PM2.5 (1.014 [1.01 to 1.019]), and closeness to roads with noise levels above 75 dB (0.993 [0.992 to 0.995]). Higher SMI prevalence was also associated with a higher percentage of people above 24 years old (1.002 [1.002 to 1.003]), a higher percentage of ethnic minorities (1.002 [1.001 to 1.002]), and more deprived areas.Mean SMI prevalence at LSOA level in major conurbations mirrored the national associations with a few exceptions. In Birmingham, higher average SMI prevalence at LSOA level was positively associated with proximity to an urban green space with a lake (0.992 [0.99 to 0.998]). In Liverpool and Manchester, lower SMI prevalence was positively associated with road traffic noise ≥75 dB (1.012 [1.003 to 1.022]). In Birmingham, Liverpool, and Manchester, there was a positive association of SMI prevalence with distance to flood zone 3 (land within flood zone 3 has ≥1% chance of flooding annually from rivers or ≥0.5% chance of flooding annually from the sea, when flood defences are ignored): Birmingham: 1.012 [1.000 to 1.023]; Liverpool and Manchester: 1.016 [1.006 to 1.026]. In contrast, in Leeds, there was a negative association between SMI prevalence and distance to flood zone 3 (0.959 [0.944 to 0.975]). A limitation of this study was because we used a cross-sectional approach, we are unable to make causal inferences about our findings or investigate the temporal relationship between outcome and risk factors. Another limitation was that individuals who are exclusively treated under specialist mental health care and not seen in primary care at all were not included in this analysis.ConclusionsOur study provides further evidence on the significance of socioeconomic associations in patterns of SMI but emphasises the additional importance of considering environmental characteristics alongside socioeconomic variables in understanding these patterns. In this study, we did not observe a significant association between green space and SMI prevalence, but we did identify an apparent association between green spaces with a lake and SMI prevalence. Deprivation, higher concentrations of air pollution, and higher proportion of ethnic minorities were associated with higher SMI prevalence, supporting a social-ecological approach to public health prevention. It also provides evidence of the significance of spatial analysis in revealing the importance of place and context in influencing area-based patterns of SMI.

Characterizing Divergent Experiences with the Same Wildfire: Insights from a Survey of Households in Evacuation, Postfire Flood Risk, and Unaffected Areas After the 2019 Museum Fire

Abstract Households can experience the same wildfire event differently depending on the kinds of risks posed to them, among other factors. These experiences can influence support or opposition for local forest management. We administered a mixed-mode survey to households across three distinct groups with different sources of risk associated with the 2019 Museum Fire in Flagstaff, Arizona: houses in the wildfire evacuation areas, houses in the postfire flood-risk area, and unaffected houses within the city limits. Survey responses from 787 respondents confirmed that households experienced the Museum Fire and its associated risks differently and revealed continued support for active forest management in the Flagstaff area. Experiences, trustworthiness of information, and support for specific forest management outcomes varied across our sample populations, indicating that tailored communication may be needed for households who experience different risk associated with the same event. We conclude with considerations for communicating with the public in postfire environments. Study Implications There are significant differences in perspectives and attitudes between directly and indirectly affected households that experienced the same wildfire, including varied trust in information sources and engagement in communication about wildfire and flood risk. Therefore, more targeted communication about wildfire and postfire risk and forest management that tailors outreach based on different household experiences is needed. Future efforts to investigate populations affected by wildfire should account for potentially diverse household experiences and consider how that may affect communication about forest management during windows of opportunity after wildfire events. Differentiating approaches to risk communication is particularly important during compound disasters (e.g., a flood that occurs within a wildfire-affected area) to ensure information is shared by the right outlet for a given population as risk sources begin to layer temporally.

A methodological approach for mapping and analysing cascading effects of flooding events

Current local or regional flood risk assessments, as required by the EU flood risk directive, rarely account for cascading effects due to interdependencies between critical infrastructures. However, it is essential to consider these effects, as they may severely impact areas outside the immediate flood risk area. The main purpose is to present and problematise a method (AB-CEM) for mapping and analysing cascading effects due to floods, aiming at also being relevant for other spatially widespread hazards. The method development and the pilot study, in Sweden, reveal that there is a prominent practical need for methods for mapping and analysing critical infrastructure interdependencies and cascading effects. Another key finding is that the method process and its results can serve as an important basis for decision-making about proactive and reactive efforts related to geographically extensive hazards. We further conclude that there is a recurring problem regarding sensitive and secret data. More specifically, the conflicting interests of information availability and information security concerning critical infrastructures, which needs to be resolved at the national level and communicated through clear guidelines. The method is a much-needed step towards accounting for cascading effects of floods in practice.

Participatory Geographic Information System (PGIS) Mapping; an Integrated Flood Management Tool in the Flood Prone Areas of Lower Kano Plains, Kisumu County, Kenya

For decades floods have continuously threatened communities’ livelihoods and destroyed properties and the ecosystem as a whole. In an attempt to minimize the destructive nature of these events, different flood mitigation strategies have been employed. Nevertheless, the challenge with these strategies employed is that they seldom give audience to the people affected, instead focusing on technical solutions. Thus the need to employ an integrated floods management approach that puts local knowledge into consideration. Although some studies have emphasized the need to have this incorporated with technical expertise to get more lasting solutions to the negative flood effects, much work still needs to be done. This study was conducted in the Nyando sub-catchment, of Lower Kano Plains in Kisumu County, Kenya. The study utilized both qualitative and quantitative methodologies. Stratified sampling technique was used to select the three flood-prone areas in Lower Kano Plains, namely, Nyando, Miwani, and Lower Nyakach as study sites. Simple random sampling technique was then used to select 100 households for the survey while Purposive sampling was used to select the key informants. Methods of data collection included questionnaires, key informant interviews, focus group discussions (FGDs), GIS-integrated participatory community mapping, and desk reviews. The main research findings were that 65.93% of respondents in this study live downstream of river Nyando, of which 60.00% resided in Nyando Sub County, thus exacerbating the risk to floods. The study concluded that Miwani, Nyando, and Lower Nyakach were found to be the flood risk areas of Lower Kano Plains.

Disaster risk management: Focused to flood hazard and its impact in Pakistan

<p>Recently, weather and climate related extreme occasions circumstances such as droughts, heat waves, bush fires and floods are increasing around the world. South Asian countries, that have the highest annual average number of people physically affected by natural hazards mostly by floods. Most popular flood risk areas are urban areas because there is rapid urbanization infrastructure and uncontrolled development. These are the most common factors that It results in more people being vulnerable to natural hazards floods than ever before. Pakistan is positioned within a hazard-prone region and is exposed to several kinds of natural disasters such as earthquakes, cyclones, landslides, droughts and floods but still it lacks proper disaster management policies. Floods in Pakistan bring a great destruction both in urban and rural areas. This paper focuses on some of the key benefits that may be achieved in presence of a proper disaster management framework or policies and also it proposes latest technologies to deal with hazards. This paper also demonstrates a simple concept that may be adopted and implemented immediately, that may help to minimize the impact economically, socially and environmentally.</p>

Modeling and Risk Analysis of Dam-Break Flooding in a Semi-Arid Montane Watershed: A Case Study of the Yabous Dam, Northeastern Algeria

The risk related to embankment dam breaches needs to be evaluated in order to prepare emergency action plans. The physical and hydrodynamic parameters of the flood wave generated from the dam failure event correspond to various breach parameters, such as width, slope, and formation time. This study aimed to simulate the dam breach failure scenario of the Yabous dam (northeast Algeria) and analyze its influence on the related areas (urban and natural environments) downstream of the dam. The simulation was completed using the sensitivity analysis method to assess the impact of breach parameters and flooding on the dam break scenario. The flood wave propagation associated with the dam break was simulated using the one-dimensional HEC-RAS hydraulic model. This study applied a sensitivity analysis of three breach parameters (slope, width, and formation time) on five sites selected downstream of the embankment dam. The simulation showed that the maximum flow of the flood wave recorded at the level of the breach was 8768 m3/s, which gradually attenuated along the river course to reach 1972.7 m3/s at about 8.5 km downstream the dam. This study established the map of flood risk areas that illustrated zones threatened by the flooding wave triggered by the dam failure due to extreme rainfall events. The sensitivity analysis showed that flood wave flow, height, and width revealed positive and similar changes for the increases in adjustments (±25% and ±50%) of breach width and slope in the five sites. However, flood wave parameters of breach formation time showed significant trends that changed in the opposite direction compared to breach slope and width. Meanwhile, the adjustments (±25% and ±50%) of the flood hydrograph did not significantly influence the flood parameters downstream of the dam. In the present study, the HEC-RAS 1-D modeling demonstrated effectiveness in simulating the propagation of flood waves downstream of the dam in the event of dam failure and highlighted the impact of the breach parameters and the flood hydrographical pattern on flood wave parameters.

Predicting future urban growth scenarios and potential urban flood exposure using Artificial Neural Network-Markov Chain model in Miami Metropolitan Area

Due to the increased coastal population growth and urbanization along with rising sea levels, more people and properties will be under increased flood risk globally. Urban growth prediction models are now used to simulate potential future urban growth scenarios but impacts of future flood risks due to sea level rise (SLR) on future development have not been studied well. Owing to its higher predictive accuracy, this study employs the Multi-layer perceptron (MLP) based Artificial Neural Network-Markov Chain (ANN-Markov) model to simulate three future urban growth scenarios (business as usual (BAU), planned growth (PG), and sustainable growth (SG)) in Miami Metropolitan Area (Miami MSA), and three SLR scenarios (1 ft, 2 ft, and 3 ft) were spatialized with the current high-risk flooding (HF) zone to delineate future flood risk areas. Then the flood risks of future urban development in each growth scenario were assessed at both regional (MSA) and local (County) scales. Results show that current land use plan (PG) slightly decreases flood risks at the regional scale but not always at the local scale compared to the without growth regulation scenario (BAU). Nevertheless, flood risks in the PG scenario are significantly higher compared to the without growth in the HF zone scenario (SG). Urban growth scenario predictions can help prepare for and understand the SLR impacts.

Linking flood risk perceptions and psychological distancing to climate change: A case study of rural communities along Indus and Chenab rivers, Pakistan

Climate change contributes to the increasing frequency and severity of floods around the globe. Developing countries are being disproportionally affected. In 2010, Pakistan witnessed one of the worst floods in its history. One-fifth of the country was severely affected, leading to major economic losses and casualties. Thus, it is imperative to understand climate change and flood risk perception for designing flood risk reduction and climate change adaptation strategies. This study examines flood risk perception and psychological distance to climate change of rural communities along the Indus and Chenab rivers in Muzaffargarh district, Pakistan. Flood risk perception was measured using three main components, i.e., awareness about floods, worry (about floods), and preparedness. Psychological distance to climate change was quantified using five dimensions, i.e., psychological, geographic, social, temporal, and uncertainty. Yamane sampling method was used, and 365 samples were collected. The data was collected using household surveys from rural communities. Descriptive statistics, chi-square test, ANOVA-test, and Pearson's correlation were performed. Results indicate that overall flood risk perception and psychological distance to climate change was moderate in a high flood risk area. A negative correlation was also observed between uncertainty and worry. Regression analyses indicate a strong positive influence of homeownership on flood risk perception and the converse impact on the psychological distance to climate change. This study can help integrate the philosophies of disaster risk reduction and climate change adaptation. The paper also highlights the need to improve risk communication strategies to help people understand climate change and its impacts, adopt precautionary measures, and reduce flood risks.

Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul

Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This process is developed based on the rainfall-runoff model and deep learning model. A model-driven method was devised to construct the accurate physical model with combined inland-river and flood control facilities, such as pump stations and underground storages. To calibrate the rainfall-runoff model, data of gauging stations and pump stations of an urban stream in August 2020 were used, and the model result was presented as an R2 value of 0.63~0.79. Accurate flood warning criteria of the urban stream were analyzed according to the various rainfall scenarios from the model-driven method. As flood forecasting and warning in the urban stream, deep learning models, vanilla ANN, Long Short-Term Memory (LSTM), Stack-LSTM, and Bidirectional LSTM were constructed. Deep learning models using 10-min hydrological time-series data from gauging stations were trained to warn of expected flood risks based on the water level in the urban stream. A forecasting and warning method that applied the bidirectional LSTM showed an R2 value of 0.9 for the water level forecast with 30 min lead time, indicating the possibility of effective flood forecasting and warning. This case study aims to contribute to the reduction of casualties and flood damage in urban streams and accurate flood warnings in typical urban flood risk areas of South Korea. The developed urban flood forecasting and warning process can be applied effectively as a non-structural measure to mitigate urban flood damage and can be extended considering watershed characteristics.

Assessment of flood-risk areas using random forest techniques: Busan Metropolitan City

Assessment of flood-risk areas using random forest techniques: Busan Metropolitan City

Geospatial analysis of river flood hazard assessment

Floods are one of the most damaging natural disasters which occur frequently in the world. They occur every year in Malaysia due to higher precipitation rates, river meandering, and heavily populated suburban areas. Monsoon rains are the major cause of floods and occur two times per year. The heavy floods in the Kelantan River Basin have shown an increasing trend in recent years. Terrain characteristics of the land and meteorological properties of the region are the main natural factors for this disaster. In this study, the Kuala Krai district of the Kelantan River is selected to be reviewed as the case study for flood risk analysis. Geographical Information System (GIS) integrated with Multicriteria Decision Analysis (MCDA) can be used to evaluate the potential flood risk areas. Historically flooded areas can be extracted from the satellite images to determine flood causing factors for the analysis. At the end of the study, a map of flood risk areas can be generated and validated to assist decisionmakers on the menace posed by the disaster. The expected results obtained from the study will help the guidance city planners and administrators to choose safe sites for construction and development. This study will also help to provide flood mitigation and quick relief response for the red zone areas which are more likely to hit badly from flood hazards. The study will help the Malaysian government to reduce natural flood risks in Malaysia which are one of the top causes of damage.

Flood Susceptibility Zones and Their Relations to Public Perceived Flood Risk Areas

Flood Susceptibility Zones and Their Relations to Public Perceived Flood Risk Areas

Avaliação da capacidade de modelos hidrológicos-hidrodinâmicos de larga escala para mapear o risco de inundação no sul do Brasil

ABSTRACT Mapping flood risk areas is important for disaster management at the local, regional, and national scales. The aim of this study was to evaluate the ability of large-scale models to obtain flood hazard maps. The models were compared to the estimates developed by the Brazilian Geological Survey (CPRM) for different return periods (RP). The floods were evaluated for the municipalities of Uruguaiana, Montenegro and São Sebastião do Caí in the Rio Grande do Sul state. It was shown that the flood mapping generated by MGB covers larger areas (greater than 1000 km2; Siqueira et al. 2018), with a lower cost of obtaining for large scales. The - Hit Rate of the regional and continental MGB model versions with the CPRM maps ranged from about 40% to 90% in different cities, and the Hit Rate between the regional model and the CPRM map increased with the increased return period floods. The continental model compatibility was similar for all analyzed RPs. Our results suggest the agreement in terms of Hit Rate of current large-scale hydrological-hydrodynamic models to assess flood hazard.

Current and future projections of flood risk dynamics under seasonal precipitation regimes in the Hyrcanian Forest region

The main aim of this research is to predict the impact of seasonal precipitation regimes on flood hazard applying machine learning models. For this purpose, twelve static variables and eight rainfall dynamics variables for 2050s (RCP 2.6 and 8.5) were used as conditioning factors. Four machine learning algorithms including K-Nearest Neighbour (KNN), Extremely Randomize Trees (ERT), Random Forest (RF) and Oblique-Random Forest (ORF) were used to model flood risk. Considering the area under curve (AUC) and other indices, the ORF was the most optimal model. The AUC of the KNN, ERT, RF and ORF for the validation datasets were 0.85, 0.90, 0.89 and 0.92 respectively. The results showed that under two RCPs, spatial distribution of high flood risk areas will change in the future and the trends will be different from the current. These results could provide valuable insights in simulating, predicting and reducing future flood risk.

Mapping of the Flood Distribution in an Urban Environment: The Case of Palma (Mallorca, Spain) in the First Two Decades of the 21st Century

The City of Palma de Mallorca has been historically affected by flood events, with the latest flood of great magnitude occurring in 1962. After decades without large flooding events, since the start of the 21st century, 28 floods have been identified within the city boundaries. As change in the spatial location of these events has been observed, a research to identify where the floods are located nowadays and which causes can be related to such distribution has been undertaken. Six types of floods were identified and the events classified accordingly, with the deficient planned infrastructures, flat urban areas without defined drainage and coastal sectors the three most common types. The results highlight the importance of the urban sprawl and malpractices related to flood risk areas, thus increasing the occurrence of floods of less spatial impact but with greater disturbance on the daily activity of the city inhabitants. Nevertheless, some uncertainties are identified in this research, demonstrating the need of further investigations.

Flood risk and damage analysis in urban areas of Zimbabwe. A case of 2020/21 rain season floods in the city of Gweru

Urban flooding has become a topical issue in the 21st century as flood frequency and severity in urban areas have increased due to the changing climate. The study assessed the flood risk of areas in the city of Gweru and the damage from the 2020/21 rainfall season flooding. A mixed methods research design anchored by both qualitative and quantitative data probing techniques was employed in this study. Geographic information system, questionnaire, interviews and observations were adopted for data collection. Microsoft excel 2013 and ArcMap 10.5 software were used for data analysis. The majority of residential areas are located in flood-prone areas. Disregard of city by-laws, corruption and poor planning were major drivers of establishment of residential stands in flood-risk areas. Property damage and crop destruction among others were major socio-economic impacts of the 2020/21 rainfall season flooding incident. The study concludes that areas historically reserved as urban carbon-sinks prior to the current climate change geographical period have now been developed into residential areas because of the lull in flooding events in recent years. The study recommends that urban development authorities should cognize frequent auditing of development activities and due-respect of urban by-laws in order to avoid development in flood-risk areas.

Child health and wellbeing in a changing climate

Abstract Children are one of the most sensitive groups to climate change, along with the elderly and those in small island developing states, according to the Intergovernmental Panel on Climate Change. UNICEF estimates that more than half a billion children are estimated to live in extremely high flood risk areas. Children are vulnerable to both the sudden events linked to climate change (e.g., flooding, heatwaves, and wildfires) as well as slower-onset impacts (e.g., water scarcity, shifting agricultural zones, rising sea levels, and increasing disease-vector ranges). Climate change has enormous potential to undermine development gains and livelihood options for today's children. This presentation will provide an overview of impacts on child health and development of both the sudden climate change effects, including extreme weather events, extreme heat, and effects on natural systems; and consequences of the changing climate, such as displacement, migration, and social instability. The trauma of living through a climate-related disaster can have substantial mental health impacts on children and youth. Some key challenges towards to studying and modelling these impacts will be shared. Finally, action and commitments to protect children from climate change will be highlighted.

Flood vulnerability reduction. Case study: Tazlău river basin, downstream of its confluence with Tazlău Sărat

The issue of water management as well as its impact on the community has been both a challenge and a concern of the European Union, which lead to adopting the Directive 2007/60/EC with the purpose of establishing a framework for the assessment and management of flood risks, by which the member states had to identify the areas for which they conclude that potential significant flood risk exist or likely to occur and to develop flood risk management plans and measures to reduce the consequences of flooding. In this respect, the Siret Water Basin Administration has declared the area of the Tazlău river, as a significant flood risk area (A.P.F.S.R.) (*** A.B.A Siret). The Tazlău River is a left-bank tributary to the Trotuș River, passing through the ten communes and partially through the outside the built-up areas of Onești city. Recent studies have shown the benefits of reservoirs and floodplains, as these provide the most effective way to buffer the effect of floods by slowing and spreading the discharge of a river (Băloi, 1980). By restoring and improving an existing lateral floodplain in the Tazlău River basin, in order to store part of the floodwaters that cause significant adverse consequences, will reduce the flood vulnerability of its crossings.

Geomorphological Appraisal of the Madogo-Mororo Floods using Neuro-Fuzzy Inference and the Multinomial Logistic Regression Models

The Tana River County has had serious issues relating to management of its riparian corridors and gazetted catchments. Floods have been an annual episode in Madogo-Mororo areas, with disastrous consequences every time they occur. Lives of both humans and livestock have been lost in every such episode, alongside the destruction of infrastructures and damages to properties, valued in the range of millions of Dollars. The causes do not directly relate to the rainfall volume registered whenever it rains, but rather to myriads of anthropogenic, geomorphological and hydrological variables. A combined statistical - and Artificial Intelligence-aided mapping of the study area was deemed a necessity as a DESK-TOP planning tool for flood risk assessment in the Study Area, as well as for detailed flood-risk mapping. The objective of this study was to identify all these three components, which involved mapping the longitudes, latitudes, elevations, soil types prevalent, vegetation density, grainsize of rocks or soil particles deemed prevalent in the surficial geology, culvert types constructed in the areas visited, hosing types, nearness of settlement to rivers, erosional intensity status of an area, as the input variables, to predict the output variable, flooding probability of an area. Visits were made to the Madogo -Mororo areas and the coded weightings of the aforementioned variables assigned, as deemed appropriate by researcher. For instance, soil erosion intensity was coded as 1, 2, and 3, with the numbers respectively coding for low intensity, moderate intensity and high intensity. The R software modeling libraries were used in this exercise. The neuro-fuzzy algorithms were then used to develop prediction models for floods, based on these coded parameters to assess then accuracy levels of the algorithm test datasets were used, generating over 98.4 percent levels of accuracies. The logistic regression models were then used, for comparison, also recording accuracy levels of 98.4 percent. This affirmed the supremacy of fuzzy logic and logistic regression as useful assessment tools for floods probability mapping in the study area. A code value of 1, represented a high probability of an area being flooded (flood risk areas), and a code of 2 was used to represent high-probability of moderate risk to flooding, whereas the value of 3 signaled a risk-free high-probability in an area. These probability codes were then plotted in a GIS map to help display the geospatial models of flood risk in Madogo and Mororo areas. The study notes the role of human activities and geomorphic factors in exacerbating the propensity to flooding episodes in the study area. Summarily, this study validates the use of neuro-fuzzy inference and logistic regression as dependable mapping tools for flood episodes in Madogo Mororo areas, used alongside the GIS techniques.