Investigation of land use changes in rural areas using MCDM and CA-Markov chain and their effects on water quality and soil fertility in south of Iran

Ronizi, Saeed Reza Akbarian; Negahban, Saeed; Mokarram, Marzieh

doi:10.1007/s11356-022-21951-y

Cited by 8 publications

(5 citation statements)

References 53 publications

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“…What the present study contributes to the literature is a novel method for modeling obesity trends over time. Markov chains were utilized to observe the trajectories of the cohort 31–33 . The models allowed precise projections into the future (Figure 3).…”

Section: Discussionmentioning

confidence: 99%

“…Markov chains were utilized to observe the trajectories of the cohort. 31 , 32 , 33 The models allowed precise projections into the future (Figure 3 ). 34 , 35 , 36 Additionally, the novel use of bootstrap simulations allowed for accurate quantification of the variance within these Markov chain projections (Figures 3 , 4 , 5 ).…”

Section: Discussionmentioning

confidence: 99%

“…Markov chains were utilized to observe the trajectories of the cohort. [31][32][33] The models allowed precise projections into the future (Figure 3). [34][35][36] Additionally, the novel use of bootstrap simulations allowed for accurate quantification of the variance within these Markov chain projections T A B L E 1 Summary of patient characteristics (demographic, socioeconomic, and laboratory measurements), stratified by individuals with weight loss versus no weight loss.…”

Section: Model Construction and Statistical Analysismentioning

confidence: 99%

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Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

Huang

2023

Obesity Science & Practice

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ImportanceThe prevalence of obesity among United States adults has increased from 34.9% in 2013–2014 to 42.8% in 2017–2018. Developing methods to model the increase of obesity over‐time is a necessity to know how to accurately quantify its cost and to develop solutions to combat this national public health emergency.MethodsA cross‐sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017–2020) was conducted in individuals who completed the weight questionnaire and had accurate data for both weight at the time of survey and weight 10 years ago. To model the dynamics of obesity, a Markov transition state matrix was created, which allowed for the analysis of weight transitions over time. Bootstrap simulation was incorporated to account for uncertainty and generate multiple simulated datasets, providing a more robust estimation of the prevalence and trends in obesity within the cohort.ResultsOf the 6146 individuals who met the inclusion criteria, 3024 (49%) individuals were male and 3122 (51%) were female. There were 2252 (37%) White individuals, 1257 (20%) Hispanic individuals, 1636 (37%) Black individuals, and 739 (12%) Asian individuals. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight. A total of 2411 (39%) individuals lost weight, and 3735 (61%) individuals gained weight. 87 (1%) individuals were underweight (BMI <18.5), 2058 (33%) were normal weight (18.5 ≤ BMI <25), 1376 (22%) were overweight (25 ≤ BMI <30) and 2625 (43%) were in the obese category (BMI >30).ConclusionUnited States adults are at risk of transitioning from normal weight to the overweight or obese category. Markov modeling combined with bootstrap simulations can accurately model long‐term weight status.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Model Construction and Statistical Analysismentioning

confidence: 99%

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Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

Huang

2023

Obesity Science & Practice

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“…This method has proven effective in examining the succession of interactions between multiple climate drivers and events (e.g., Sedlmeier et al., 2016) and could be directly ported to the analysis of CCI (Challenge 3). For example, Markov chains have recently been used to predict the impact of drought changes on water and soil quality (Ronizi et al., 2022). At the same time, the discretization of states implicit in the Markov chain analysis can be problematic in the context of a continuum of hydrological drivers and associated CCIs.…”

Section: Key Methods For Investigating CCI Patterns and Relationshipsmentioning

confidence: 99%

Uncovering the Dynamics of Multi‐Sector Impacts of Hydrological Extremes: A Methods Overview

de Brito,

Sodoge,

Fekete

et al. 2024

Earth's Future

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Hydrological extremes, such as droughts and floods, can trigger a complex web of compound and cascading impacts (CCI) due to interdependencies between coupled natural and social systems. However, current decision‐making processes typically only consider one impact and disaster event at a time, ignoring causal chains, feedback loops, and conditional dependencies between impacts. Analyses capturing these complex patterns across space and time are thus needed to inform effective adaptation planning. This perspective paper aims to bridge this critical gap by presenting methods for assessing the dynamics of the multi‐sector CCI of hydrological extremes. We discuss existing challenges, good practices, and potential ways forward. Rather than pursuing a single methodological approach, we advocate for methodological pluralism. We see complementary or even convergent roles for analyses based on quantitative (e.g., data‐mining, systems modeling) and qualitative methods (e.g., mental models, qualitative storylines). The data‐driven and knowledge‐driven methods provided here can serve as a useful starting point for understanding the dynamics of both high‐frequency CCI and low‐likelihood but high‐impact CCI. With this perspective, we hope to foster research on CCI to improve the development of adaptation strategies for reducing the risk of hydrological extremes.

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“…This method has proven effective in examining the succession of interactions between multiple climate drivers and events (e.g., Sedlmeier et al, 2016) and could be directly ported to the analysis of CCI (Challenge 3). For example, Markov chains have recently been used to predict the impact of drought changes on water and soil quality (Ronizi et al, 2022). At the same time, the discretization of states implicit in the Markov chain analysis can be problematic in the context of a continuum of hydrological drivers and associated CCIs.…”

Section: Multivariate Statisticsmentioning

confidence: 99%

Uncovering the dynamics of multi-sector impacts of hydrological extremes: a methods overview

Brito

Sodoge

Fekete

et al. 2023

Preprint

View full text Add to dashboard Cite

Hydrological extremes, such as droughts and floods, can trigger a complex web of compound and cascading impacts due to interdependencies between coupled natural and social systems. However, current decision-making processes typically only consider one impact and disaster event at a time, ignoring causal chains, feedback loops, and conditional dependencies between impacts. Analyses capturing these complex patterns across space and time are thus needed to better inform effective adaptation planning. This perspective paper aims to bridge this critical gap by presenting methods for assessing the dynamics of the multi-sector compound and cascading impacts (CCI) of hydrological extremes. We discuss existing challenges, good practices, and potential ways forward. Rather than pursuing a single methodological approach, we advocate for methodological pluralism. We see complementary roles for analyses building on quantitative (e.g. data-mining, systems modeling) and qualitative methods (e.g. mental models, qualitative storylines). We believe the data-driven and knowledge-driven methods provided here can serve as a useful starting point for understanding the dynamics of both high-frequency CCI and low-likelihood but high-impact CCI. With this perspective, we hope to foster research on CCI to improve the development of adaptation strategies for reducing the risk of hydrological extremes.

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Investigation of land use changes in rural areas using MCDM and CA-Markov chain and their effects on water quality and soil fertility in south of Iran

Cited by 8 publications

References 53 publications

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017–2020

Uncovering the Dynamics of Multi‐Sector Impacts of Hydrological Extremes: A Methods Overview

Uncovering the dynamics of multi-sector impacts of hydrological extremes: a methods overview

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