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

Hasanuzzaman, Md.; Islam, Aznarul; Bera, Biswajit; Shit, Pravat Kumar

doi:10.1016/j.pce.2022.103198

Cited by 33 publications

(12 citation statements)

References 74 publications

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“…The approach of Khalaf et al [ 57 ] was third with 85.9% accuracy. The remaining approaches [ 26 , 41 , 44 ] all had an 85% accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Their solution is based on a combination of a quantum-inspired co-evolutionary algorithm, along with random rotation direction, and a Hamming adaptive rotation angle. As for river flood monitoring, Hasanuzzaman et al [ 41 ] compared the performance of three Machine Learning (ML) algorithms for flood susceptibility mapping. Their dataset consisted of 500 historical flood points with twelve influencing factors (elevation, rainfall, slope, etc.).…”

Section: Related Workmentioning

confidence: 99%

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An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Boulouard

Ouaissa

et al. 2022

Sensors

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River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.

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“…The approach of Khalaf et al [ 57 ] was third with 85.9% accuracy. The remaining approaches [ 26 , 41 , 44 ] all had an 85% accuracy.…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Boulouard

Ouaissa

et al. 2022

Sensors

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“…The study area is one of the regions with the highest rainfall in Vietnam. Heavy rains in the mountains cause a great flood to the coastal plain along with landslides (Hasanuzzaman et al, 2022; Lin et al, 2022). In the study area, precipitation in 2021 ranged from 1578 to 2731 mm.…”

Section: Methods and Techniquesmentioning

confidence: 99%

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Nguyen

Dang

Bui

et al. 2023

Transactions in GIS

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Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi‐hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi‐hazard susceptibility mapping framework, by combining two natural hazards—flooding and landslides—in the North Central region of Vietnam. This was accomplished using support vector machines, random forest, and AdaBoost. The input data consisted of 4591 flood points, 1315 landslide points, and 13 conditioning factors, split into training (70%), and testing (30%) datasets. The accuracy of the models' predictions was evaluated using the statistical indices root mean square error, area under curve (AUC), mean absolute error, and coefficient of determination. All proposed models were good at predicting multi‐hazard susceptibility, with AUC values over 0.95. Among them, the AUC value for the support vector machine model was 0.98 and 0.99 for landslide and flood, respectively. For the random forest model, these values were 0.98 and 0.98, and for AdaBoost, they were 0.99 and 0.99. The multi‐hazard maps were built by combining the landslide and flood susceptibility maps. The results showed that approximately 60% of the study area was affected by landslides, 30% by flood, and 8% by both hazards. These results illustrate how North Central is one of the regions of Vietnam that is most severely affected by natural hazards, particularly flooding, and landslides. The proposed models adapt to evaluate multi‐hazard susceptibility at different scales, although expert intervention is also required, to optimize the algorithms. Multi‐hazard maps can provide a valuable point of reference for decision makers in sustainable land‐use planning and infrastructure development in regions faced with multiple hazards, and to prevent and reduce more effectively the frequency of floods and landslides and their damage to human life and property.

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“…Moreover, the random forest model can handle high dimensional data without feature selection and can identify more critical factors. Hasanuzzaman et al analyzed drivers of floods using three models, namely extreme gradient boosting, naive bayes, and random forest, and ascertained the superiority of the random forest model [27]. Zhang et al estimated aboveground carbon storage of woody vegetation by the random forest model and verified that it performs well [28].…”

Section: Introductionmentioning

confidence: 99%

Determinants of Aboveground Carbon Storage of Woody Vegetation in an Urban–Rural Transect in Shanghai, China

et al. 2023

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Carbon storage of urban woody vegetation is crucial for climate change mitigation. Biomass structure and species composition have been shown to be important determinants of carbon storage in woody vegetation. In this study, allometric equations were used to estimate the aboveground carbon storage of urban woody vegetation along an urban–rural transect in Shanghai. A random forest model was developed to evaluate the importance scores and influence of species diversity, canopy cover, species evenness, and tree density on aboveground carbon storage. The results showed that tree density, canopy cover, species diversity, species evenness, and aboveground carbon storage of urban woody vegetation vary with the degree of urbanization and urban–rural environment. In addition, the Bayesian optimization algorithm optimized the random forest model parameters to enhance model accuracy, and good modeling results were demonstrated in the study. The R2 was at 0.61 in the testing phase and 0.78 in the training phase. The root mean square errors (RMSEs) were 0.84 Mg/ha of carbon in the testing phase and 0.57 Mg/ha in the training phase, which is indicative of a low error of the optimized model. Tree species diversity, canopy cover, species evenness, and tree density were found to correlate with aboveground carbon storage. Tree density was the most important contributor, followed by species diversity and canopy cover, and species evenness was the least effective for aboveground carbon storage. Meanwhile, the results of the partial dependence analysis indicated the combination of factors most conducive to aboveground carbon storage at a tree density of 2200 trees/ha, canopy cover of 50%, species diversity of 1.2, and species evenness of 0.8 in the transect. The findings provided practical recommendations for urban forest managers to adjust the structure and composition of woody vegetation to increase carbon storage capacity and reduce greenhouse gas emissions.

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

Cited by 33 publications

References 74 publications

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Determinants of Aboveground Carbon Storage of Woody Vegetation in an Urban–Rural Transect in Shanghai, China

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