The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Sustainability, Год журнала: 2023, Номер 15(13), С. 10543 - 10543

Опубликована: Июль 4, 2023

Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts control essential to lessen these effects safeguard populations. By utilizing its capacity handle massive amounts of data provide accurate forecasts, deep learning has emerged as potent tool for improving prediction control. The current state applications in forecasting management is thoroughly reviewed this work. review discusses variety subjects, such the sources utilized, models used, assessment measures adopted judge their efficacy. It assesses approaches critically points out advantages disadvantages. article also examines challenges with accessibility, interpretability models, ethical considerations prediction. report describes potential directions deep-learning research enhance predictions Incorporating uncertainty estimates into integrating many sources, developing hybrid mix other methodologies, enhancing few these. These goals can help become more precise effective, which will result better plans forecasts. Overall, useful resource academics professionals working on topic management. reviewing art, emphasizing difficulties, outlining areas future study, it lays solid basis. Communities prepare destructive floods by implementing cutting-edge algorithms, thereby protecting people infrastructure.

Язык: Английский

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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

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Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Mapping Through Machine Learning Approaches

Earth Systems and Environment, Год журнала: 2024, Номер 8(1), С. 63 - 81

Опубликована: Янв. 1, 2024

Abstract This study harnessed the formidable predictive capabilities of three state-of-the-art machine learning models—extreme gradient boosting (XGB), random forest (RF), and CatBoost (CB)—applying them to meticulously curated datasets topographical, geological, environmental parameters; goal was investigate intricacies flood susceptibility within arid riverbeds Wilayat As-Suwayq, which is situated in Sultanate Oman. The results underscored exceptional discrimination prowess XGB CB, boasting impressive area under curve (AUC) scores 0.98 0.91, respectively, during testing phase. RF, a stalwart contender, performed commendably with an AUC 0.90. Notably, investigation revealed that certain key variables, including curvature, elevation, slope, stream power index (SPI), topographic wetness (TWI), roughness (TRI), normalised difference vegetation (NDVI), were critical achieving accurate delineation flood-prone locales. In contrast, ancillary factors, such as annual precipitation, drainage density, proximity transportation networks, soil composition, geological attributes, though non-negligible, exerted relatively lesser influence on susceptibility. empirical validation further corroborated by robust consensus XGB, RF CB models. By amalgamating advanced deep techniques precision geographical information systems (GIS) rich troves remote-sensing data, can be seen pioneering endeavour realm analysis cartographic representation semiarid fluvial landscapes. findings advance our comprehension vulnerability dynamics provide indispensable insights for development proactive mitigation strategies regions are susceptible hydrological perils.

Язык: Английский

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A systematic review of the flood vulnerability using geographic information system

Heliyon, Год журнала: 2022, Номер 8(3), С. e09075 - e09075

Опубликована: Март 1, 2022

The world has faced many disasters in recent years, but flood impacts have gained immense importance and attention due to their adverse effects. More than half of global destruction damages occur the Asia region, which causes losses life, damage infrastructure, creates panic conditions among communities. To provide a better understanding hazard management, vulnerability assessment is primary objective. In this case, central construct analysis assessment. Many researchers defined different approaches methods understand how geographic information systems assess associated risk. Geographic track predict disaster trend mitigate risk damages. This study systematically reviews methodologies used measure floods vulnerabilities by integrating system. Articles on from 2010 2020 were selected reviewed. Through systematic review methodology five research engines, discovered difference tools techniques that can be bridged high-resolution data with multidimensional methodology. reviewed several components directly examined shortcomings at levels. contributed indicator-based approach gives system provides an effective environment for mapping precise disaster.

Язык: Английский

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Flood susceptibility zonation using advanced ensemble machine learning models within Himalayan foreland basin

Natural Hazards Research, Год журнала: 2022, Номер 2(4), С. 363 - 374

Опубликована: Июнь 14, 2022

Floods are considered as one of nature's most destructive fluvio-hydrological extremes because the massive damage to agricultural land, roads and buildings human fatalities. Rapid development unplanned infrastructural conveniences anthropogenic activities, frequency intensity floods have been accelerated in recent years. Therefore, flood susceptibility analysis is an important management approach. Identification areas has performed by applying advanced machine learning (ML) algorithms (random forest (RF), support vector (SVM) extreme gradient boosting (XGBoost)) at lower part Raidak river basin. The maps generated based on 14 different conditioning factors. Models evaluated a conventional way using ROC (receiver operating characteristics) curve. AUC value above 0.80 for all models XGBoost depicts highest efficacy (AUC ​= ​0.92). Friedman test Wilcoxon Signed rank used measure statistical variances among applied models. proficiently show that upper basin less probable region whereas eastern some middle parts high probability. Around 27% area (285.39 sq.km) within highly prone (based model) due fast changing dynamic landscape large scale intervention. outcomes this research will definitely assist local administrators take proper sustainable plans reduction future damages.

Язык: Английский

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A comparison of performance measures of three machine learning algorithms for flood susceptibility mapping of river Silabati (tropical river, India)

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2022, Номер 127, С. 103198 - 103198

Опубликована: Июль 11, 2022

Язык: Английский

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Evaluating the effects of landscape fragmentation on ecosystem services: A three-decade perspective

Ecological Informatics, Год журнала: 2023, Номер 77, С. 102283 - 102283

Опубликована: Авг. 29, 2023

Язык: Английский

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Improvement of flood susceptibility mapping by introducing hybrid ensemble learning algorithms and high-resolution satellite imageries

Natural Hazards, Год журнала: 2023, Номер 119(1), С. 1 - 37

Опубликована: Авг. 5, 2023

Язык: Английский

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Vulnerability assessment of forest ecosystem based on exposure, sensitivity and adaptive capacity in the Valmiki Tiger Reserve, India: A geospatial analysis

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102494 - 102494

Опубликована: Янв. 22, 2024

Forests are becoming increasingly vulnerable to a range of climatic and non-climatic stressors. Thus, the forest vulnerability assessment is crucial for identifying potential risks enhancing resilience. The present study attempts explore in protected area Valmiki Tiger Reserve (VTR), India. A ecosystem index (FEVI) was constructed using its three components (exposure, sensitivity adaptive capacity) site-specific indicators. Exposure, capacity indices were integrated prepare map. map validated through receiver operating characteristic curve (ROC) confusion metrics found reliable. results revealed that total Reserve, largest under moderate (48.36%), followed by high (32.28%) low (19.36%). Madanpur, Raghia, lower part Harnatanr Chiutaha identified as most ranges VTR. High exposure, attributed continuous monitoring devising effective management strategies essential reducing resilience Urgent policy interventions also required promoting ecotourism minimizing dependency communities on forest. systematic framework employed may be applied diverse geographical regions sites suggesting conservation restoration strategies.

Язык: Английский

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Determination of flood probability and prioritization of sub-watersheds: A comparison of game theory to machine learning

Journal of Environmental Management, Год журнала: 2021, Номер 295, С. 113040 - 113040

Опубликована: Июнь 18, 2021

Язык: Английский

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