Flooded Areas Research Articles

Temporal geomorphic modifications and climate change impacts on the lower course of the São Francisco River, Brazil

Monitoring human intervention and understanding temporal geomorphic modifications in hydrographic basins require continuous data collection. This study aimed to investigate the geomorphological and hydroclimatic changes in the lower course of the São Francisco River, Brazil from 1986 to 2020, utilizing Cloud-based satellite image processing techniques for mapping and identifying flooded areas through the calculation of Modified Normalized Difference Water Index (MNDWI). The findings reveal a significant increase in sedimented areas within the river channel, occupying 10.64 km2 in 2020, which represents a 22% increment compared to 1986. The river has experienced a territorial reduction of approximately 20%, with decreased Modified Normalized Difference Water Index values indicating shallowing due to possible sediment contributions. Rainfall and flow data exhibited a positive correlation with Modified Normalized Difference Water Index and a negative correlation with sedimentation values, indicating their influence on the river's geomorphology. Furthermore, the Alagoas bank displayed 96 transects, with an average penetration of 86.36 m into the river for each transect, highlighting hydrological and morphological changes. Overall, this study emphasizes the vulnerability of the São Francisco River.

Estimation of Electrical Energy Consumption in Irrigated Rice Crops in Southern Brazil

On average, 70% of the world’s freshwater is used in agriculture, with farmers transitioning to electrical irrigation systems to increase productivity, reduce climate uncertainties, and decrease water consumption. In Brazil, where agriculture is a significant part of the economy, this transition has reached record levels over the last decade, further increasing the impact of energy consumption. This paper presents a methodology that utilizes the U-Net model to detect flooded rice fields using Sentinel-2 satellite images and estimates the electrical energy consumption required to pump water for this irrigation. The proposed approach involves grouping the detected flooded areas using k-means clustering with the electricity customers’ geographical coordinates, provided by the Power Distribution Company. The methodology was evaluated in a dataset of satellite images from southern Brazil, and the results demonstrate the potential of using U-Net models to identify rice fields. Furthermore, comparing the estimated electrical energy consumption required for irrigation in each cluster with the billed energy values provides valuable insights into the sustainable management of rice production systems and the electricity grid, helping to identify non-technical losses and improve irrigation efficiency.

Автоматизация процесса подготовки производства земляных работ

Theoretical and materials for the creation of analog-discrete terrain models and their use in solving engineering problems are presented. With their help, it is possible to model areas of flooding, calculate the receipt of solar radiation, plan the construction of infrastructure facilities and other tasks. Various variants of the interpretation of the construction of analytical and discrete terrain models are considered. Block modules of algorithms for constructing, processing and visualizing analog terrain models are given. The samples of structured data sources for their creation are given. A relief modeling technique based on the use of logical-mathematical and matrix methods of big data processing is proposed.

The Influence of the Main Factors on the Accuracy of Hydrological Modelling of Flooded Lands

This paper proposes a general methodological approach to hydrological modeling for determining the areas of flooded land in the plain part of the Dniester riverbed, the second largest river in Ukraine. The purpose of the study is the selection of parameters for modeling flooded zones in the plain section of the Dniester riverbed, taking into account the rising water level caused by the freshet that occurred in the western part of Ukraine on 22–24 June 2020. The proposed study examines the workflow of hydrological modeling of the flooded land zone and the main components of this scheme: the construction of the DTM, considering the roughness of the riparian territory by Manning’s coefficients, and indications of water rise. In the experiment, the influence of DTM reproduction resolution and Manning coefficients was analyzed, and their optimal values were selected, which allowed obtaining the parameters of hydrological modeling with a higher probability. The identified flooding areas were tested using a high-resolution space image during the flood in June 2020. The distance between the profiles affects not only the value of the modeling area but also their detail. The accuracy of the modeled flooded area is 5.1% for a 5 m interval between the profiles, 6.9% for 50 m, 8.2% for 100 m, and 10.8% for 200 m. These results allow determining the degree of influence of the distance between intervals on the modeling accuracy. Using different values of Manning’s coefficients for individual sections with different bedding surfaces and the selected spacing between profiles, which was 50 m, the accuracy of the modeling was investigated. After the modelling, the simulated flood areas were obtained in vector form, which allows for determining their areas and comparing them with the test flooded area. In the presented research, the RMSE of determining the flooded areas is about 5%. The test area of 600.6 hectares was determined with an accuracy of 0.8%.

Assessment of Landsat-8 and Sentinel-2 Water Indices: A Case Study in the Southwest of the Buenos Aires Province (Argentina).

The accuracy assessment of three different Normalized Difference Water indices (NDWIs) was performed in La Salada, a typical lake in the Pampean region. Data were gathered during April 2019, a period in which floods occurred in a large area in the Southwest of the Buenos Aires Province (Argentina). The accuracy of the estimations using spaceborne medium-resolution multi-spectral imaging and the reliability of three NDWIs to highlight shallow water features in satellite images were evaluated using a high-resolution airbone imagery as ground truth. We show that these indices computed using Landsat-8 and Sentinel-2 imagery are only loosely correlated to the actual flooded area in shallow waters. Indeed, NDWI values vary significantly depending on the satellite mission used and the type of index computed.

Coastal Flooding Associated with Hurricane Irma in Central Cuba (Ciego de Ávila Province)

Irma was a major hurricane that developed during the 2017 season. It was a category 5 on the Saffir–Simpson Hurricane wind scale. This hurricane caused severe damage in the Caribbean area and the Florida Keys. The social, economic, and environmental impacts, mainly related to coastal flooding, were also significant in Cuba. The maximum limits of coastal flooding caused by this hurricane were determined in this research. Field trips and the use of the GPS supported our work, which focused on both the northern and southern coasts of the Ciego de Ávila province. This work has been critical for improving coastal flooding scenarios related to a strong hurricane, as it has been the first experience according to hurricane data since 1851. Results showed that the Punta Alegre and Júcaro towns were the most affected coastal towns. The locals had never seen similar flooding in these places before. The differences between flood areas associated with Hurricane Irma and previous modeled hazard scenarios were evident (the flooded areas associated with Hurricane Irma were smaller than those modeled for categories 1, 3, and 5 hurricanes). The effects of this hurricane on the most vulnerable coastal settlements, including the impacts on the archeological site “Los Buchillones”, were also assessed.

One Health Approach to Leptospirosis: Dogs as Environmental Sentinels for Identification and Monitoring of Human Risk Areas in Southern Brazil.

Leptospirosis has been a neglected, widespread and reemerging zoonotic disease of global importance. The One Health holistic approach combining human, animal and environmental health has been important for control and prevention of zoonotic disease. An urban municipality in Southern Brazil with a high prevalence of leptospirosis was selected by convenience, with asymptomatic human and canine individuals serologically tested using MAT against 30 Leptospira spp. serovars. Epidemiological questionnaires were assessed along with the historical national database, with associated risk factors to dog and human seropositivity analyzed using multiple logistic regression. The spatial distribution of retrospective human leptospirosis cases was analyzed using a Kernel map and overlapped dog seropositivity and historical flooding areas, demonstrating that domestic and asymptomatic dogs may be important environmental sentinels for leptospirosis in such urban areas, even in the absence of human seropositivity and low canine seropositivity. Associated risk factors for leptospirosis in dogs and humans according to multiple regression included healthy dogs (p = 0.02, odds ratio-OR = 0.17, confidence interval of 95%CI 0.03-0.71) with indoor access (p = 0.059, OR = 5.12, 95%CI 1.10-37.7) and human males (p = 0.042, OR = 2.44, 95%CI 1.08-6.11) with presenting calf pain (p = 0.005, OR = 3.14, 95%CI 1.46-7. 14), jaundice (p < 0.001, OR = 4.61, 95%CI 2.11-10.10) and renal failure (p = 0.008, OR = 4.49, 95%CI 1.49-13.76). The greater the rain precipitation (above 3 mm of average annual precipitation), the higher the number of leptospirosis cases in humans. In conclusion, dog active serosurvey and rain precipitation should be systematically reported and plotted altogether as a basis for the early detection and monitoring of human risk areas. Such findings may serve as a basis for public health policies in Brazil and other endemic countries worldwide.

ANALYZING THE IMPACT OF SEA LEVEL RISE ON COASTAL FLOODING AND SHORELINE CHANGES ALONG THE COAST OF LOUISIANA USING REMOTE SENSORY IMAGERY

Abstract. Sea level rise poses risks to coastal areas which is increasingly rendering such areas susceptible to flood and shoreline retreat. Notably, coastal areas like Southern Louisiana located along the Gulf of Mexico has experienced endangering events of land subsidence due to flood inundations resulting from incessant distribution of hurricanes and tropical storms. This research therefore employed remote sensing data to analyze the impacts of sea level rise on coastal flooding and shoreline retreat along the coast of Louisiana. That is, by assessing Sentinel-2 imagery data to evaluate flood prone and flood extent areas particularly during the Louisiana floods and Hurricane Harvey. Based on this, the results show most of the inland parishes in coastal Louisiana such as Assumption, St. James, Livingston, Lafourche and Terrebonne were within high flood risk zones of about 9.3. These parishes also suffered severe damage in terms of affected croplands, potentially flooded areas and affected urban areas. On the other hand, most of the parishes in close proximity to the waterbodies such as the Gulf of Mexico were interestingly within low flood risk zones of about 6.1 suggesting proximity to waterbodies not being the only indicating factor of a flood prone area. This research also highlights that Louisiana's shorelines are rapidly receding at a rate that could result in the loss of one million acres of the state’s land in the next four decades. Hence, the results from this research are anticipated to contribute to sustainable shoreline setback plans and mitigative strategies to protect Louisiana's coast.

Анализ восстановления растительного покрова в пределах Шуруповских карьеров на основе данных дистанционного зондирования

The article presents an analysis of restoration processes within two quarries located in the northwest of the Shurupovsky Khutor in the Frolovsky district of the Volgograd region. The described methods of revegetation analysis within the quarry-dump complexes included images from the USGS Earth Explorer portal (US Geological Survey). The Landsat-5 TM satellite images were used in July 2007, Landsat 8 OLI_TIRS was used in July 2015, and Landsat 9 OLI_TIRS described the status in July 2022. Using the ArcGIS geoinformation system 10.3.1, we considered multispectral images of different times and calculated the Normalized Difference Vegetation Index (NDVI) for 2007, 2015, and 2022. As a second index, we calculated the NDVI trend for 2007–2015 and 2015–2022. Based on the results of interpretation, the assessment of changes in the vegetation cover of quarry-dump complexes was carried out. As a result, it was revealed that the vegetation cover of quarries demonstrated different restoration pathways despite their close territorial location. By 2022, the NDVI of the eastern quarry did not exceed 0.2, and the NDVI of the western quarry increased to 0.5. However, in general, there is a positive NDVI trend in the territories of both quarries. Resulting from the data obtained, NDVI maps and NDVI trend maps for the studied quarries were prepared. Tables for calculating changes in the areas of flooding and vegetation cover of the Shurupov quarries for 2007, 2015, and 2022 were created in MS Excel.

Conceptualising flood warning system for connected vehicles

Floods are a common natural hazard in Malaysia during the monsoon season. It affects millions of people each year that leads to severe deaths and infrastructure destruction. In recent time, flood warning system (FWS) has been a notable topic but it has not been extensively implemented in Malaysia. In this study, we developed a FWS that can interface with connected vehicles in order to provide alerts to drivers while also sending warnings to end users. This type of FWS enables vehicles to connect with one another within a particular radius to broadcast flood information via long range (LoRa) communication technology. When the water level rises over a certain point, the system sends a warning to drivers indicated through a mobile application. Drivers have the option to take alternative route, reducing the likelihood of damage when driving into or near a flooded area. The developed application demonstrates that the warning was able to be instantly displayed to the driver if there is a significant increase in water level. Experimental evidence shows that the driver was able to receive the water level alert and a visual interpretation of the immediate area affected by flood through the application.

STABLITY OF TEMPORARILY PLACED LARGE SANDBAGS AGAINST WAVES

In Japan, Typhoon No. 15 (Faxai) hit the Kanto region on September 5, 2019, causing massive flooding damage due to the collapse of seawalls (Suzuki et al., 2020). In the Fukuura district of Yokohama City, the parapet of the seawall collapsed and large overtopping waves inundated an area 0.75 km inland from the seawall, resulting in a flooded area of 0.8 km2. Since the typhoon season lasts until November, there was a possibility that typhoons would continue to hit the area after that. Therefore, with the parapet collapsed, there was a possibility of major flooding damage again. Under such conditions, temporary overtopping measures were necessary. In Japan, large sandbags are commonly installed in such cases. However, there are many unknowns regarding the stability of sandbags against waves. Even if they are installed, they are likely to be scattered by waves. In this study, emergency recovery measures and subsequent responses in the Fukuura district due to Typhoon No. 19 were examined. Furthermore, the stability of sandbags was examined through large hydraulic experiments.

Apply MIKE models to study the impacts of climate change on flooding and design adaptation measures in Can Tho City in the Vietnamese Mekong Delta.

Can Tho City in the Mekong Delta usually has pluvial and fluvial floods every year. The situation will get worst in the future under climate change. This study aims (i) to apply the MIKE 11, MIKE 21, and MIKE FLOOD model to simulate the impact of climate change scenarios on flooding in Can Tho city (ii) to suggest climate change adaptation measures for sustainable social-economic activities of the city. We considered the combined effect of sea-level rise including RCP 2.6, RCP 4.5, and RCP 8.5, and changes in climate factor (i.e., precipitation) for the years 2025, 2030, and 2050. The results show that the flood model is appropriate to simulate the flood. The flooded areas of the city under the scenarios of RCP 2.6 and RCP 8.5 will be increased from 2025 to 2050, and RCP 4.5 will be increased progressively from the year 2025 to 2030 and reduced to 2050. In the scenario of RCP 8.5, the flooded area of the city will be gradually increased in 2025, 2030, and 2050 at 3,559 ha, 3,719 ha, and 4,476 ha, respectively. Fifteen adaptation measures to climate change from 2021 to 2050 are suggested.

Urban flood risk assessment based on DBSCAN and K-means clustering algorithm

Urban flood risk assessment plays a crucial role in disaster risk reduction and preparedness. It is essential to mitigate flood risks and establish a comprehensive analysis of factors influencing flood risk, as well as classify risk levels, in order to provide a clear model for risk assessment. This article aims to propose an efficient assessment method that can classify urban flood risk levels and assist cities in flood risk management, particularly in identifying high-risk areas. The study area chosen for this method is the municipal district of Fuzhou City, located in Fujian Province, China. The proposed method utilizes the Urban Flood Risk Assessment Index, which is developed based on the risk-vulnerability framework. It integrates the combinatorial empowerment method, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and the K-means algorithm to cluster the quantitative risk factors, enabling a comprehensive analysis of the risk results. The findings demonstrate that areas characterized by intense extreme rainfall, lower elevation, gradual slope, high runoff coefficient, and high population density tend to exhibit higher flood risk. Moreover, the dominant factors contributing to high risk in different regions vary. The results obtained from this method align well with the distribution of historical flood points, indicating the effectiveness of the risk map prepared using this approach. In comparison to the results obtained from the single clustering method and the TOPSIS method used in traditional risk assessment, the proposed method can successfully identify high-risk urban flood areas. Consequently, this method offers a valuable scientific basis for urban flood prevention and control planning.

A new approach based on biology-inspired metaheuristic algorithms in combination with random forest to enhance the flood susceptibility mapping

Flash floods are one of the worst natural disasters, causing massive economic losses and many deaths. Creating a flood susceptibility map (FSM) that pinpoints the areas most at risk of flooding is a crucial non-structural solution for managing floods. This study aimed to assess the efficacy of combinations of the random forest (RF) model with three biology-inspired metaheuristic algorithms, namely invasive weed optimization (IWO), slime mould algorithm (SMA), and satin bowerbird optimization (SBO), for flood susceptibility mapping in Estahban town, Iran. Initially, synthetic-aperture radar (SAR) (Sentinel-1) and optical (Landsat-8) satellite images were integrated to monitor the flooded areas during the July 2022 monsoon in the study area. A dataset of 509 flood occurrence points was created to identify flood-prone areas using remote sensing techniques, considering the monitored flood areas. The dataset also included twelve flood-related criteria: topography, land cover, and climate. The holdout method was employed for modeling, with a ratio of 70:30 used for the train/test split. Data pre-processing techniques were conducted to improve model performance, including determining criteria importance and addressing multicollinearity issues using certainty factor (CF), multicollinearity, and information gain ratio (IGR) methods. Then FSM was prepared using RF, RF-IWO, RF-SBO, and RF-SMA models. The findings of this research revealed that the RF-IWO model was the best predictive model of flood susceptibility modeling, with root-mean-square-error (RMSE) (0.211 and 0.0.27), mean-absolute-error (MAE) (0.103 and 0.15), and coefficient-of-determination (R2) (0.821 and 0.707) in the training and testing phases, respectively. Receiver operating characteristic (ROC) curve analysis of FSM revealed that the most accurate models were the RF-IWO (area under the curve (AUC) = 0.983), RF-SBO (AUC = 0.979), RF-SMA (AUC = 0.963), and RF (AUC = 0.959), respectively. Integrating biology-inspired computing algorithms with machine learning algorithms presents a novel approach to enhancing the accuracy of FSMs.

Height Above Nearest Drainage (HAND) as a Model for Rapid Flood Inundation Mapping Based on Remote Sensing and Geographic Information Systems in the Kapuas Sintang Sub Watershed

This study aims to map the flood inundation and the extent of the inundation in the study area using the HAND model. The data used in this study is DEM. The DEM is used to generate a hydrologic framework, including flow accumulation, drainage network, flow direction, elevation, and flow distance. The method used in this study is the HAND descriptor. The analysis in this study used spatial hydrological analysis and hypsometric analysis using zonal statistical tables in ArcGIS. Based on the results of the analysis of height above the nearest drainage it is known that the Kapuas Sintang sub-watershed has five classes of inundation, namely very high inundation, high inundation, moderate inundation, low inundation, and no inundation. Very high, high, and moderate inundation classes are spread over three sub-districts, namely Sintang, Dedai, and Tempunak sections. Sintang District has the widest distribution, followed by Dedai District and Tempunak District is the narrowest. Prediction of inundation area and flood area with HAND can be used to improve the new mapping model without involving additional data sources. The HAND model is a nice and simple tool that is useful for inundation studies as well as in inundation area prediction.

Integrated Approach for the Study of Urban Expansion and River Floods Aimed at Hydrogeomorphic Risk Reduction

Urbanization in flood-prone areas is a critical issue worldwide. The historical floods, the urban expansion in terms of building footprint, the extent and construction period of inundated buildings with reference to two representative floods (5–6 November 1994 and 24–25 November 2016), and the ground effects and dynamics of these events were investigated in the cities of Garessio, Ceva, and Clavesana, along the Tanaro River (NW Italy). An integrated approach based on historical data analysis, photograph interpretation, field surveys, and GIS investigations was adopted, and novel metrics for quantitative analysis of urbanization and flood exposure at the individual-building scale were introduced. The considered cities were hit by damaging floods several times over the last centuries and experienced an increase in built-up surface after the mid-19th century, especially between the 1930s and 1994. The 1994 and 2016 high-magnitude floods highlighted that urban expansion largely occurred in flood-prone areas, and anthropogenic structures conditioned flood propagation. One of the rare Italian cases of the relocation of elements exposed to floods is documented. This research aims to emphasize the relevance of information on past floods and urbanization processes for land planning and land management and the need for land use planning for flood control to forbid new urban expansion in potentially floodable areas. The outcomes represent an essential knowledge base to define effective and sustainable management measures to mitigate hydrogeomorphic risk.

A flash flood detected area using classification-based image processing for sentinel-2 satellites data: A case study of Zafaraana Road at Red Sea

Natural crises manifested in floods and droughts are counted as the most severe impacts of climate change on the world. In this regard, flash floods are the most common cause of economic and human losses worldwide. However, the present study focuses on the flash flood-affected area between Zafaarana and Ras Ghareb coastal roads. Sentinel-2 satellite images of recent years before and after the flash flood have been utilized to detect flooded areas and investigate their environmental conditions.Initially, the captured images were pre-processed to compare the environmental conditions before and after flooding. Consequently, the Normalized Difference Water Index (NDWI) was utilized to classify water bodies in different bands. Finally, an image difference feature (IDF) model with computation of per-pixel features, merging image disparities, and calculation of the characteristic value phases was constructed to extract various image differences after photo processing, that's to identify flooded pixels in the images and assess their performance in the proposed model. The proposed IDF model was compared by rating each model on the same test set, while changing the training set. In conclusion, the proposed algorithm shows an accuracy of 98.9%, which is a better flood image processing technique than other methods. The insights from this research will help decision makers in structuring their rescue strategies and evacuation maps during and before the environmental crisis.

Assessment of flood in Nandigama, Andhra Pradesh, using hydrological and hydraulic models

AbstractThe rapid and frequent mapping of flood areas is crucial for monitoring and mitigating flood disasters. This study aimed to devise a flood inundation map for Nandigama City, located in the state of Andhra Pradesh, India. To analyze flood inundation, the HEC‐HMS hydrologic model was used to assess the effects of land‐use changes on runoff. The analysis of land use and land cover (LULC) revealed a significant increase in urban expansion around the study area over the last 30 years. The rise in urbanization and impervious surfaces has led to an 11.85% increase in the average peak discharge, from 4.39 m3/s in 2000 to 4.91 m3/s in 2020. These findings can provide significant insights for urban policymakers in the planning and management of flood control infrastructure in Nandigama.

Appalling pregnancy outcomes in flood-affected areas.

Respected Madam, Floods are one of the most common types of natural disasters leading to enormous morbidity and mortality in a developing country like Pakistan. Pakistan is ranked 9th among flood-affected countries worldwide and in 2022 floodwater has submerged over one-third of the country in water, killing over 1,000 and impacting 33 million people. (1) Flood-affected areas serve as ideal breeding grounds for pathogens, severely disrupting livelihoods and impacting the health of the vast majority and adversely influencing the lives of pregnant women. In 2021 Erin et al. reported the impact of the flood on pregnancy, health, birth outcomes and postpartum mental health. They stated that pregnant women in the first trimester are at high risk of having low birth weight (LBW) babies and a reduction in fetal growth rate in areas of major floods. (2) This is not the only study to show these results. A systematic review shows the increased prevalence of preterm birth, at the same time, highlighting increasing incidents of gestational hypertension, increased rate of LBW birth, gestational hypertension, preeclampsia and eclampsia after the flood. (3) A study published after the 2012 floods in Pakistan raised serious concerns regarding diarrhoea, skin &amp; eye infections, leptospirosis, malaria, leishmaniasis, respiratory infections and hepatitis being prevalent in flooded areas of Pakistan. (4) The major concern author wants to highlight is the health of pregnant women and growing fetuses being particularly vulnerable to direct and indirect impacts of natural disasters. The poor standards of hygiene in camps set up for women displaced by the floods also contribute to pregnancy-related complications including sepsis. A recent report by the United Nations Population Fund (UNFPA) has stated s a need for providing healthcare facilities to almost 650,000 pregnant women in flood-affected areas in Pakistan. (5) Flood-affected areas need medical aid to mitigate pregnancy-related complications in relief camps. But no research done on the Pakistani population in flood-affected areas directly highlights the outcome of flood on pregnancy and fetal well-being. The author suggests a data collection and assessment approach and reporting system to evaluate pregnancy-related complications in flood-affected areas; so that future policy, clinical and research strategies are based on research and get adopted to mitigate the effects of future floods on pregnancy outcomes. In addition to this, trained healthcare workers, robust health systems and gender-sensitive responses should be set in place in responding to flood emergencies.

Antimicrobial Activities of Actinomycetes Isolated From Flooded and Unflooded Soils

Introduction: Flooding may cause the microbial population in the soil to move from one area to another. Actinomycete, a type of soil microbe, has the most commercial value due to its ability to produce secondary metabolites. This study aimed to elucidate the antimicrobial activities of actinomycetes isolated from flooded and unflooded areas. Methods: Soils samples were collected from flooded areas in Dabong, Kelantan, and unflooded areas in Jeli, Kelantan. Three isolation methods were used to isolate actinomycetes; Sonication, Centrifugation and Chloramine T. The isolated strains were screened for morphological characteristics based on their growth pattern (spore formation), colony color, aerial and substrate mycelia color, and soluble pigment formation in the growth medium. Morphologically different strains were tested against Escherichia coli and Candida albicans for its antibacterial and antifungal activities. Results: A total of 970 actinomycete strains were isolated from soil samples (570 strains from flooded soil and 400 strains from unflooded soils). Only 281 strains were morphologically different. Thirty actinomycete strains were tested for antibacterial and antifungal activity. Seventeen of these inhibit at least one test microorganism. Conclusion: In conclusion, our observations reveal that the soil samples obtained from flooded areas display a wide variety of actinomycetes, as evident from their morphological characteristics. This finding suggests that the flooded soil areas possess a higher diversity of actinomycetes compared to non-flooded soil areas. Furthermore, we found that 57% of the tested actinomycete strains exhibited activity against at least one test organism, indicating their potential for future research.