Flood susceptibility mapping using hybrid models optimized with Artificial Bee Colony

Abstract Floods represent a significant threat to human life, property, and agriculture, especially in low-lying floodplains. This study assesses flood susceptibility in the Brahmaputra River basin, which spans China, India, Bhutan, and Bangladesh—an area notorious for frequent flooding due to the saturation of river water intake capacity. We developed and evaluated several innovative models for predicting flood susceptibility by employing Multi-Criteria Decision Making (MCDM) and Machine Learning (ML) techniques. The models showed robust performance, evidenced by Area Under the Receiver Operating Characteristic Curve (AUC-ROC) scores exceeding 70% and Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) scores below 30%. Our findings indicate that approximately one-third of the studied region is categorized as moderately to highly flood-prone, while over 40% is classified as low to very low flood-risk areas. Specific regions with high to very high flood susceptibility include Dhemaji, Dibrugarh, Lakhimpur, Majuli, Darrang, Nalbari, Barpeta, Bongaigaon, and Dhubri districts in Assam; Coochbihar and Jalpaiguri districts in West Bengal; and Kurigram, Gaibandha, Bogra, Sirajganj, Pabna, Jamalpur, and Manikganj districts in Bangladesh. Owing to their strong performance and the suitability of the training datasets, we recommend the application of the developed MCDM techniques and ML algorithms in geographically similar areas. This study holds significant implications for policymakers, regional administrators, environmentalists, and engineers by informing flood management and prevention strategies, serving as a climate change adaptive response within the Brahmaputra River basin. 

Evaluating Flood Susceptibility in the Brahmaputra River Basin: An Insight into Asia's Eastern Himalayan Floodplains Using Machine Learning and Multi-Criteria Decision-Making

A new avenue to improve the performance of integrated modeling for flash flood susceptibility assessment: Applying cluster algorithms

Flood potential mapping by integrating the bivariate statistics, multi-criteria decision-making, and machine learning techniques

Abstract This research is analyzed the flood hazard using two ensemble models in Mazandaran Province, Iran and assess how the experience contributed to community flood preparedness by household surveys. For this purpose, two approaches of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Attributive Border Approximation Area Comparison (MABAC) combined with Analytical Hierarchy Process (AHP) and weight of evidence (WOE) models were applied. Applying eight effective factors, including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood risk zoning map was prepared with the help of Geographic Information System. The area under the receiver operating characteristic curve (AUROC) for test sample set was 87.14% and 83.24% for TOPSIS-based and MABAC-based models, respectively. Regression analysis found a negative correlation between past flood experience and flood preparedness of residents, consistent with cluster analysis. This indicates that local people had not learned much from previous experiences of flood catastrophes.

Application of MCDM methods for flood susceptibility assessment and evaluation the impacts of past experiences on flood preparedness

Golestan Province is one of the most vulnerable areas to catastrophic flood events in Iran. The flood severity in this region has grown dramatically during the last decades, demanding a major investigation. Accordingly, an authentic map providing detailed information on floods is required to reduce future flood disasters. Three ensemble models produced by the combination of Evaluation Based on Distance from Average Solution (EDAS) and Multilayer Perceptron Neural Network (MLP) with Frequency Ratio (FR), and Weights of Evidence (WOE) are used to quantify the map flood susceptibility in Golestan Province, in the north of Iran. Ten flood effective criteria, namely altitude, slope degree, slope aspect, plan curvature, distance from rivers, Topographic Wetness Index (TWI), rainfall, soil type, geology, and land use, are considered for the modeling process. The flood zonation maps are validated by the receiver operating curve (ROC). The results show that the most precise model is MLP-FR (AUROC = 0.912), followed by EDAS-FR-AHP (AUROC = 0.875), and EDAS-WOE-AHP (AUROC = 0.845). The high accuracies of all methods applied to illustrate their capability in predicting flood susceptibility in future studies.

/pdf/determining-flood-zonation-maps-using-new-ensembles-of-multi-37pd72t6.pdf

Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network

This study investigates the distributions of velocity and Reynolds stress in an artificial pool-riffle sequence for two entrance slopes of 5° and 20° in a laboratory and compares the results with a similar pool-riffle sequence in a gravel-bed river in central Iran. Quadrant analysis is applied to find out the contributions of different events on turbulent flow structures. At an entrance slope of 5°, the velocity distribution follows an exponential trend without flow separation, but for the entrance slope of 20°, the velocity distribution indicates a S-shaped pattern with flow separation. Variation of entrance slope does not influence the convex form of Reynolds stress (RS) distribution, but it influences the location of the maximum value of RS and the flow separation zone length. The results reveal that outward and ejection are the dominant events for both field and laboratory settings. The sweep displays a decreasing trend from near the bed toward the water surface, however, the outward contribution indicates an increasing trend from the bed toward the water surface. The agreement between laboratory and field results in velocity and Reynolds stress will help the river engineers to better manage complex fluvial processes. Bursting process events depend on the aspect ratio for the same entrance pool slope. The agreement in the results of velocity and Reynolds stress distributions and the dominant events of bursting process in the laboratory and field are encouraging for better restoration of rivers and decreasing the cost of projects.

/pdf/turbulence-characteristics-in-mild-and-steep-entrance-slopes-1ff8dz62.pdf

Turbulence Characteristics in Mild and Steep Entrance Slopes of Pool-Riffle Sequences

Background & Objective: Long non-coding RNAs play critical roles in the initiation and development of gastric cancer (GC). The aim of this study was to assess the expression of NKILA and NFKB genes and the relationship between their expressions with clinical characteristics in gastric cancer. Also, relative expression of lncRNAsNKILA, compared NFKB in GC tissues was evaluated. 
Materials & Methods: This cross-sectional study was performed on 50 gastric formalin-fixed paraffin-embedded tumor tissue samples and 50 normal tissues. The RNA level of lncRNAsNKILA and NFKB genes was assessed using the quantitative real-time polymerase chain reaction. B2M was used as an internal control. The 2 -ΔΔCq method was used to determine the expression fold changes. 
Results: A significant association was observed between the levels of NKILA, in gastric tumor tissues compared with normal tissues (Mean = 2.087, p= 0.0484). The relative expression of the NFKB gene revealed no statistically significant difference between the gastric tumor tissues compared with normal tissues (P=0.3728). In addition, clinicopathologic data compared with NKILA and NFKBmRNA expression levels in gastric cancer tissues showed no significant association. Here, we found no significant association between the level of NKILA expression compared with NFKB mRNA level in gastric cancer tissues (R=0.03; P=0.2355). 
Conclusion: Our results showed that NKILA had a significant association with GC. Our findings also revealed that NKILA expression was not correlated with NFKB mRNA level in GC tissues.

Evaluation of NKILA lncRNA and NFκB Genes Expression in an Iranian Gastric Cancer Patient Compared with Healthy Tissue and the Relationship between Clinicopathological Features

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Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network

Application of MCDM methods for flood susceptibility assessment and evaluation the impacts of past experiences on flood preparedness

Turbulence Characteristics in Mild and Steep Entrance Slopes of Pool-Riffle Sequences

Evaluation of NKILA lncRNA and NFκB Genes Expression in an Iranian Gastric Cancer Patient Compared with Healthy Tissue and the Relationship between Clinicopathological Features