Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling

Janizadeh, Saeid; Vafakhah, Mehdi; Kapelan, Zoran; Dinan, Naghmeh Mobarghaee

doi:10.1080/10106049.2021.1996641

Cited by 22 publications

(9 citation statements)

References 54 publications

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“…Multivariate Adaptive Regression Splines (MARS) was selected as an extension of linear regression that can capture non‐linearities and interactions between variables (Friedman, 1991). Finally, Extreme Gradient Boosting (XGBoost) was selected as a tree‐based algorithm optimized via parallel processing, tree pruning, efficient missing value handling, and regularization to avoid overfitting or bias (Janizadeh et al., 2021; Ma et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

Quantifying the Safe Operating Space for Land‐System SDG Achievement via Machine Learning and Scenario Discovery

et al. 2023

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A sustainable land sector is key to achieving several of the 17 Sustainable Development Goals (SDGs) and 169 targets (UN, 2015) representing the shared environmental, social, and economic aspirations of all UN member nations (Herrero et al., 2021). However, the feasibility of achieving multiple SDG targets in the land sector has been questioned due to the inherent trade-offs involved and calls have been made to redouble efforts in the search for sustainable future pathways (Gao & Bryan, 2017). Multiple land-use models have been developed both as stand-alone models and as a part of multi-sectoral integrated assessment models to understand the complex relationships and dynamics underpinning land-system sustainability (

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Section: Methodsmentioning

confidence: 99%

Quantifying the Safe Operating Space for Land‐System SDG Achievement via Machine Learning and Scenario Discovery

et al. 2023

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“…The proposed framework does not provide meaningful results only for COVID-19 but also for other disasters such as global warming, earthquake, flood, cyber attack, drought etc. [19,38]. The same logic may be applied to parameter measuring before and during a disease and the changes in these parameters with high accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature selection by machine learning models to identify the public’s changing priorities during the COVID-19 pandemic

Mengüç

Aydın

2022

AIS

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People around the world have experienced fundamental transformations during mass events. The Industrial Revolution, World War II, and the collapse of the Berlin Wall are some of the cases that have caused radical societal changes. COVID-19 has also been a process of mass experiences regarding society. Determining the mass impact the pandemic has had on society shows that the pandemic is facilitating the transition to the so-called new normal. Istanbul is a multi-identity city where 16 million people have intensely experienced the pandemic’s impact. While determining the identities of cities in the world, one can see that different city structures provide different data sets. This study models a machine learning algorithm suitable for the data set we’ve determined for the 39 different districts of Istanbul and 82 different features of Istanbul. The aim of the study is to indicate the changing societal trends during the COVID-19 pandemic using machine learning techniques. Thus, this work contributes to the literature and real life in terms of redesigning cities for the post-COVID19 period. Another contribution of this study is that the proposed methodology provides clues on what people in cities consider important during a pandemic.

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“…Adaptive Regression Splines (MARS) was selected as an extension of linear regression that can capture non-linearities and interactions between variables (Friedman, 1991). Finally, Extreme Gradient Boosting (XGBoost) was selected as a tree-based algorithm optimized via parallel processing, tree pruning, efficient missing value handling, and regularisation to avoid overfitting or bias (Ma et al, 2020, Janizadeh et al, 2021.…”

Section: Meta-model Preparationmentioning

confidence: 99%

“…Recently, international collaborative modelling studies have extended the number of scenarios analysed to hundreds or even thousands (McPhail et al, 2020). For example, the ScenarioMIP (O'Neill et al, 2016) approach considered combinations of SSPs and RCPs as alternative future pathways, each modelled using a number of integrated assessment models, generating a scenario database of 1184 distinct views of the future (IPCC, 2014). Bryan et al (2016a), modelled 648 scenarios of future land-use change in Australia using the Land-Use Trade-Offs (LUTO) model but still only managed to sample the six input parameter uncertainty dimensions at between two and four levels (e.g., low, medium, high estimates).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Safe Operating Space for Land-System SDG Achievement via Machine Learning Meta-Modelling and Scenario Discovery

Khan

Nazari

Thiruvady

et al. 2022

Preprint

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We developed a machine learning based meta-model to identify sustainability pathways through rapid scenario generation and defined the safe operating space for achieving them via scenario discovery. We trained a meta-model to replicate the Land-Use Trade-Offs integrated model of the Australian land system. Latin hypercube sampling was used to create many scenarios exploring the impact of uncertainties in key drivers including future socio-economic development, climate change mitigation, and agricultural productivity at a granular level. Economic and environmental impacts were evaluated against nationally downscaled SDG targets. Scenario discovery revealed new pathways to achieving five SDG targets for 2050 which required crop yield increases above 1.78 times, a carbon price above 100 AU$ tCO2-1, a > 9% biodiversity levy on carbon plantings, and carefully regulated land-use policy. Machine learning based meta-modelling teamed with scenario discovery revealed the policy and scenario settings required for a sustainable future for the Australian land sector.

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Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling

Cited by 22 publications

References 54 publications

Quantifying the Safe Operating Space for Land‐System SDG Achievement via Machine Learning and Scenario Discovery

Quantifying the Safe Operating Space for Land‐System SDG Achievement via Machine Learning and Scenario Discovery

Feature selection by machine learning models to identify the public’s changing priorities during the COVID-19 pandemic

Quantifying the Safe Operating Space for Land-System SDG Achievement via Machine Learning Meta-Modelling and Scenario Discovery

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