Spatiotemporal analysis of wind speed via the Bayesian maximum entropy approach

Baydaroğlu, Özlem; Koçak, Kasım

doi:10.1007/s12665-018-8024-z

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…The cartographic depictions exhibit significant variation on an annual basis, not solely attributable to the characteristics mentioned above but also due to climatic variability. Similarly, it varies considerably in PET, and P. PET values depend on the air temperature and factors such as relative humidity and wind speed, which have quite chaotic properties (Baydaroğlu and Koçak, 2019). In addition to these factors, precipitation is affected by teleconnections (Villarini et al, 2013) and other synoptic scale movements (Elkhouly et al, 2023) in Iowa.…”

Section: Fuzzy Vulnerability Of Drought Indicatorsmentioning

confidence: 99%

State-Level Multidimensional Agricultural Drought Susceptibility and Risk Assessment for Agriculturally Prominent Areas

Islam¹,

Yeșilköy²,

Baydaroğlu³

et al. 2023

Preprint

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Due to the shifting climate, extreme events are being observed more frequently globally. Drought is one of the most common natural hazards that severely impacts communities in terms of economic losses and agricultural production disruption. Considering global trade, drought in an agricultural region affects the food security in other regions because of disrupted supply. Decision-makers often consult susceptibility maps when preparing mitigation plans so that the adverse impacts of a drought event can be reduced. Creating drought susceptibility maps can be demanding, requiring a lot of data (i.e., hydrological and land use), expertise, and thorough assessment to accurately picture a vulnerable region’s condition. The process also relies on complex hydrological and hydrometeorological models. The objective of this investigation is to examine the vulnerability and impact of drought and formulate maps of drought susceptibility, exposure, and risk by considering a multitude of atmospheric, physical and social indicators. Subsequent to this notion, a fuzzy logic algorithm has been devised by assigning a comprehensive array of weights to each parameter derived from an exhaustive literature review and used for a preliminary investigation for the state of Iowa. This state is located in the Corn Belt region, and its primary economic activity is agriculture. Drought susceptibility maps for the state of Iowa have been generated for the period spanning from 2015 to 2021 and validated using the Kappa coefficient. The produced drought susceptibility maps can support drought mitigation plans and decisions for communities in Iowa.

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Section: Fuzzy Vulnerability Of Drought Indicatorsmentioning

confidence: 99%

State-Level Multidimensional Agricultural Drought Susceptibility and Risk Assessment for Agriculturally Prominent Areas

Islam¹,

Yeșilköy²,

Baydaroğlu³

et al. 2023

Preprint

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“…BME is employed for estimation and prediction of different kinds of variables, such as ozone [9], soil moisture [10], rainfall [11], soil salinity [12], sea surface temperature [13] and wind [14].…”

Section: Introductionmentioning

confidence: 99%

Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy

Baydaroğlu

2020

International Advanced Researches and Engineering Journal

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Spatial and temporal distribution of PM10 is modeled by Bayesian Maximum Entropy (BME) method. It is the spatiotemporal estimation method which combines exact measurements with the secondary information by considering local uncertainties. In this study, daily average PM10 data are used to generate spatial and temporal PM10 maps. Both annual and seasonal estimations have been realized. This is the first study which concentrates on spatiotemporal distribution of PM10 for all regions of Turkey by using Bayesian Maximum Entropy method. Error variances are used as performance criteria in both seasonal and annual predictions. All prediction results stay within the limits of the confidence intervals. In addition, unknown PM10 values are estimated, including PM10 values over the seas. It is thought that the PM10 maps which show all regions of Turkey in detail are quite invaluable and informative.

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“…The results have indicated that enhancing the parameterization of growth and mortality's temperature dependence, as well as improving the spatial and temporal hydrodynamic resolution, could lead to more effective modeling of blooms in the future. Using cyanobacteria biomass as an indicator, Moe et al (2016) used Bayesian network (BN) (Baydaroğlu and Koçak, 2019) to relate future climate change and land-use management scenarios to ecological state.…”

mentioning

confidence: 99%

Modeling of Harmful Algal Bloom Dynamics and Integrated Web Framework for Inland Waters in Iowa

Baydaroğlu,

Yeşilköy,

Dave

et al. 2024

Preprint

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Harmful algal blooms (HABs) are one of the major environmental concerns, as they have various negative effects on public health, recreational services, ecological balance, wildlife, fisheries, microbiota, water quality, and economics. HABs are caused by many sources, such as water pollution based on agricultural activities, wastewater treatment plant discharges, leakages from sewer systems, natural factors like pH and light levels, and climate change impacts. While many causes of HABs are recognized, it is unknown how toxin-producing algae develop as well as the key processes and components that contribute to their weight due to the distinct algal dynamics of each lake and the variety and unpredictability of the conditions influencing these dynamics. Modeling HABs in a changing climate is essential for achieving sustainable development goals regarding clean water and sanitation. However, the lack of consistent and adequate data on HABs is a significant challenge for all these studies. In this study, we employed the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, an algal toxin, utilizing dissolved oxygen as a water quality metric and evaporation as a meteorological parameter. SINDy is a novel approach that combines sparse regression and machine learning methods to reconstruct the analytical representation of a dynamical system. Moreover, a model-driven and web-based interactive tool was created to disseminate and develop environmental education, raise public awareness on HAB events, and produce more effective solutions to HAB problems through what-if scenarios. This web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation. Users can easily share images of HABs in lakes on an interactive and user-friendly platform, allowing others to view the status of the lakes.

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Spatiotemporal analysis of wind speed via the Bayesian maximum entropy approach

Cited by 9 publications

References 33 publications

State-Level Multidimensional Agricultural Drought Susceptibility and Risk Assessment for Agriculturally Prominent Areas

State-Level Multidimensional Agricultural Drought Susceptibility and Risk Assessment for Agriculturally Prominent Areas

Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy

Modeling of Harmful Algal Bloom Dynamics and Integrated Web Framework for Inland Waters in Iowa

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