Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data

Han, Yu; Luo, Zengliang; Wang, Lunche; Ding, Xiangyi; Wang, Shaoqiang

doi:10.3390/rs15143601

Cited by 7 publications

(1 citation statement)

References 59 publications

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“…Carneiro [33], Wang [34] 2021-2023 Prediction of steel properties Zou [35], Feng [36], Liu [37], Wang [38], Qian [39] 2021-2023 Prediction of molten steel composition Wang [40] 2022 Energy efficiency Lee [41] 2021 Motor equipment load Huang [42], Yu [43] 2022-2023 Modeling and prediction of inventory change Zhou [44], Esche [45], Zhu [46], Li [47], Bouaswaig [48],…”

Section: Review Of Dynamic Problems In Complex Industrial Processesmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

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As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.

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Section: Review Of Dynamic Problems In Complex Industrial Processesmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

View full text Add to dashboard Cite

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Enhancing flood susceptibility modeling using integration of multi-source satellite imagery and multi-input convolutional neural network

Maddah,

Mojaradi,

Alizadeh

2024

Nat Hazards

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Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment

Singha,

Rana,

Pham

et al. 2024

Environ Sci Pollut Res

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Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptation. This study uses multi-sourced geospatial datasets to develop an advanced machine learning framework for flood hazard assessment in the Arambag region of West Bengal, India. The flood inventory was constructed through Sentinel-1 SAR analysis and global flood databases. Fifteen flood conditioning factors related to topography, land cover, soil, rainfall, proximity, and demographics were incorporated. Rigorous training and testing of diverse machine learning models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, and MARS algorithms, were undertaken for categorical flood hazard mapping. Model optimization was achieved through statistical feature selection techniques. Accuracy metrics and advanced model interpretability methods like SHAP and Boruta were implemented to evaluate predictive performance. According to the area under the receiver operating characteristic curve (AUC), the prediction accuracy of the models performed was around > 80%. RF achieves an AUC of 0.847 at resampling factor 5, indicating strong discriminative performance. AdaBoost also consistently exhibits good discriminative ability, with AUC values of 0.839 at resampling factor 10. Boruta and SHAP analysis indicated precipitation and elevation as factors most significantly contributing to flood hazard assessment in the study area. Most of the machine learning models pointed out southern portions of the study area as highly susceptible areas. On average, from 17.2 to 18.6% of the study area is highly susceptible to flood hazards. In the feature selection analysis, various nature-inspired algorithms identified the selected input parameters for flood hazard assessment, i.e., elevation, precipitation, distance to rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, distance to roads, and gMIS. As per the Boruta and SHAP analyses, it was found that elevation, precipitation, and distance to rivers play the most crucial roles in the decision-making process for flood hazard assessment. The results indicated that the majority of the building footprints (15.27%) are at high and very high risk, followed by those at very low risk (43.80%), low risk (24.30%), and moderate risk (16.63%). Similarly, the cropland area affected by flooding in this region is categorized into five risk classes: very high (16.85%), high (17.28%), moderate (16.07%), low (16.51%), and very low (33.29%). However, this interdisciplinary study contributes significantly towards hydraulic and hydrological modeling for flood hazard management.

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Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data

Cited by 7 publications

References 59 publications

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Enhancing flood susceptibility modeling using integration of multi-source satellite imagery and multi-input convolutional neural network

Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment

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