Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data

Water, Год журнала: 2022, Номер 14(19), С. 3069 - 3069

Опубликована: Сен. 29, 2022

Flash floods are the most dangerous kinds of because they combine destructive power a flood with incredible speed. They occur when heavy rainfall exceeds ability ground to absorb it. The main aim this study is generate flash maps using Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models in river’s floodplain between Jhelum River Chenab rivers. A total eight flood-causative physical parameters considered for study. Six based on remote sensing images Advanced Land Observation Satellite (ALOS), Digital Elevation Model (DEM), Sentinel-2 Satellite, which include slope, elevation, distance from stream, drainage density, flow accumulation, land use/land cover (LULC), respectively. other two soil geology, consist different rock formations, In case AHP, each criteria allotted an estimated weight according its significant importance occurrence floods. end, all were integrated weighted overlay analysis influence value density was given highest weight. shows that 2500 m river has values FR ranging 0.54, 0.56, 1.21, 1.26, 0.48, output zones categorized into very low, moderate, high, high risk, covering 7354, 5147, 3665, 2592, 1343 km2, Finally, results show areas or 6.68% area. Mangla, Marala, Trimmu valleys identified as high-risk area, have been damaged drastically many times by It provides policy guidelines risk managers, emergency disaster response services, urban infrastructure planners, hydrologists, climate scientists.

Язык: Английский

---

Flood susceptible prediction through the use of geospatial variables and machine learning methods

Journal of Hydrology, Год журнала: 2023, Номер 617, С. 129121 - 129121

Опубликована: Янв. 13, 2023

Язык: Английский

---

GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India

Natural Hazards Research, Год журнала: 2023, Номер 3(3), С. 420 - 436

Опубликована: Май 19, 2023

The unique characteristics of drainage conditions in the Pagla river basin cause flooding and harm socioeconomic environment. main purpose this study is to investigate comparative utility six machine learning algorithms improve flood susceptibility ensemble techniques' capability elucidate underlying patterns floods make a more accurate prediction susceptibilities basin. In present scenario, frequency area becomes high with heavy sudden rainfall, so it essential mitigation measure. At First, spatial database was built 200 locations sixteen influencing factors, its process help Geographic Information System (GIS) environment build up different models applying techniques. It has found zone using learning-based Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Reduced Error Pruning Tree (REPTree), Logistic Regression (LR), Bagging helping GIS model validation Receiver Operating Characteristic Curve (ROC). Afterward, all gate accuracy zone. calculated under very 8.69%, 14.92%, 14.17%, 12.98%, 14.65%, 13.24% 13.41% for ANN, SVM, RF, REPTree, LR Bagging, respectively. Finally, ROC curve, Standard (SE), Confidence Interval (CI) at 95 per cent were used assess compare performance models. obtained results indicate that are highly accepted Area Under (AUC) between 0.889 (LR) 0.926 (Ensemble). After application, ROC, Ensemble suited highest compared other projecting area. curve AUC values 0.918 0.926, SE (0.023, 034), narrowest CI (95 cent) (0.873–0.962, 0.859–0.993) whereas (the ROC) value (0.914, 0.919), both training datasets. ensembling, result shows susceptible located lower part area, lie 4.46 6.00 result. areas comprise low height belong Murarai I, II, Suti I II C.D. block West Bengal. current will policymakers researcher determine conditioning problems prospects.

Язык: Английский

---

Exploring the additional value of class imbalance distributions on interpretable flash flood susceptibility prediction in the Black Warrior River basin, Alabama, United States

Journal of Hydrology, Год журнала: 2022, Номер 610, С. 127877 - 127877

Опубликована: Апрель 28, 2022

Язык: Английский

---

Living with Floods Using State-of-the-Art and Geospatial Techniques: Flood Mitigation Alternatives, Management Measures, and Policy Recommendations

Water, Год журнала: 2023, Номер 15(3), С. 558 - 558

Опубликована: Янв. 31, 2023

Flood, a distinctive natural calamity, has occurred more frequently in the last few decades all over world, which is often an unexpected and inevitable hazard, but losses damages can be managed controlled by adopting effective measures. In recent times, flood hazard susceptibility mapping become prime concern minimizing worst impact of this global threat; nonlinear relationship between several causative factors dynamicity risk levels makes it complicated confronted with substantial challenges to reliable assessment. Therefore, we have considered SVM, RF, ANN—three ML algorithms GIS platform—to delineate zones subtropical Kangsabati river basin, West Bengal, India; experienced frequent events because intense rainfall throughout monsoon season. our study, adopted are efficient solving non-linear problems assessment; multi-collinearity analysis Pearson’s correlation coefficient techniques been used identify collinearity issues among fifteen factors. research, predicted results evaluated through six prominent statistical (“AUC-ROC, specificity, sensitivity, PPV, NPV, F-score”) one graphical (Taylor diagram) technique shows that ANN most modeling approach followed RF SVM models. The values AUC model for training validation datasets 0.901 0.891, respectively. derived result states about 7.54% 10.41% areas accordingly lie under high extremely danger zones. Thus, study help decision-makers constructing proper strategy at regional national mitigate particular region. This type information may helpful various authorities implement outcome spheres decision making. Apart from this, future researchers also able conduct their research byconsidering methodology

Язык: Английский

---

An effective geospatial-based flash flood susceptibility assessment with hydrogeomorphic responses on groundwater recharge

Groundwater for Sustainable Development, Год журнала: 2023, Номер 23, С. 100998 - 100998

Опубликована: Авг. 14, 2023

Язык: Английский

---

A Multi-Criteria Decision Analysis (MCDA) Approach for Landslide Susceptibility Mapping of a Part of Darjeeling District in North-East Himalaya, India

Applied Sciences, Год журнала: 2023, Номер 13(8), С. 5062 - 5062

Опубликована: Апрель 18, 2023

Landslides are the nation’s hidden disaster, significantly increasing economic loss and social disruption. Unfortunately, limited information is available about depth extent of landslides. Therefore, in order to identify landslide-prone zones advance, a well-planned landslide susceptibility mapping (LSM) approach needed. The present study evaluates efficacy an MCDA-based model (analytical hierarchy process (AHP)) determines most accurate for detecting one part Darjeeling, India. LSM prepared using remote sensing thematic layers such as slope, rainfall earthquake, lineament density, drainage geology, geomorphology, aspect, land use cover (LULC), soil. result obtained classified into four classes, i.e., very high (11.68%), (26.18%), moderate (48.87%), low (13.27%) susceptibility. It observed that entire 37.86% area zone. efficiency was validated with help receiver operating characteristics (ROC) curve, which demonstrate accuracy 96.8%, success rate curve showed 81.3%, both satisfactory results. Thus, proposed framework will natural disaster experts reduce vulnerability, well aid future development.

Язык: Английский

---

Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems

Heliyon, Год журнала: 2024, Номер 10(13), С. e33982 - e33982

Опубликована: Июль 1, 2024

Flash floods, rapid and devastating inundations of water, are increasingly linked to the intensifying effects climate change, posing significant challenges for both vulnerable communities sustainable environmental management. The primary goal this research is investigate predict a Flood Susceptibility Map (FSM) Ibaraki prefecture in Japan. This utilizes Random Forest (RF) regression model GIS, incorporating 11 variables (involving elevation, slope, aspect, distance stream, river, road, land cover, topographic wetness index, stream power plan profile curvature), alongside dataset comprising 224 instances flooded non-flooded locations. data was randomly classified into 70 % training set development, with remaining 30 used validation through Receiver Operating Characteristics (ROC) curve analysis. resulting map indicated that approximately two-thirds as exhibiting low very flood susceptibility, while one-fifth region categorized high susceptibility. Furthermore, RF achieved noteworthy an area under ROC 99.56 %. Ultimately, FSM serves crucial tool policymakers guiding appropriate spatial planning mitigation strategies.

Язык: Английский

---

Identification and mapping of groundwater recharge zones using multi influencing factor and analytical hierarchy process

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 20, 2024

The management of groundwater systems is essential for nations that rely on as the principal source communal water supply (e.g., Mohmand District Pakistan). work employed Remote Sensing and GIS datasets to ascertain recharge zones (GWRZ) in Pakistan. Subsequently, a sensitivity analysis was conducted examine impact geology hydrologic factors variability GWRZ. GWRZ determined by employing weighted overlay thematic maps derived from about drainage density, slope, geology, rainfall, lineament land use/land cover, soil types. use multi-criteria decision (MCDA) involves utilization multi-influencing factor (MIF) analytical hierarchy procedure (AHP) allocate weights selected influencing factors. MIF data found very high spanned 1.20%, covered 40.44%, moderate 50.81%, low 7.54%. In comparison, AHP technique results suggest 1.81% whole area high, 33.26 55.01% moderate, 9.92% has potential. geospatial-assisted approach helps increase conceptual knowledge resources evaluate possible zones.

Язык: Английский

---

Flood risk modelling by the synergistic approach of machine learning and best-worst method in Indus Kohistan, Western Himalaya

Geomatics Natural Hazards and Risk, Год журнала: 2025, Номер 16(1)

Опубликована: Фев. 25, 2025

Язык: Английский

---