Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions

Ebrahimi-Khusfi, Zohre; Taghizadeh–Mehrjardi, Ruhollah; Nafarzadegan, Ali Reza

doi:10.1007/s11356-020-10957-z

Cited by 24 publications

(8 citation statements)

References 94 publications

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“…Recently, ANFIS-hybrid models have been identified as a powerful tool to solve the problem of local optimization in modelling and predicting unsaturated soil hydraulic conductivity (Sihag et al, 2019), pile bearing capacity (Harandizadeh et al, 2019), soil temperature (Penghui et al, 2020), dust concentration (Ebrahimi-Khusfi et al, 2021) and dust production sources (Rahmati et al, 2020). Furthermore, they have been successfully used to predict various target variables such as air temperature (Azad et al, 2020), precipitation (Azad et al, 2019), groundwater and water river quality parameters (Azad, Manoochehri, et al, 2019;Kisi et al, 2019).…”

mentioning

confidence: 99%

New hybrid evolutionary models for spatial prediction of soil properties in Kurdistan

Mahmoudzadeh

Matinfar

Kerry

et al. 2021

Soil Use and Management

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The inherent spatial and temporal variability of soil means that its properties vary markedly with location and time (Rogerio et al., 2006). This spatial and temporal variability can happen as a result of agricultural management, land use, erosion, landforms, geomorphic elements and soil management practices (Buol et al., 1997;Denton et al., 2017). Estimating and predicting the spatial distribution of soil properties is a critical factor for sustainable land management (Castrignanò et al., 2011). Soil survey and creating soil maps are very important activities because of their

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mentioning

confidence: 99%

New hybrid evolutionary models for spatial prediction of soil properties in Kurdistan

Mahmoudzadeh

Matinfar

Kerry

et al. 2021

Soil Use and Management

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“…the study shows higher accuracy than when each algorithm is used alone. Two studies (Ebrahimikhusfi, et al, 2021a;Ebrahimi-khusfi, et al, 2021b) used an enhanced vegetation index, such as the moderate-resolution imaging spectroradiometer MODIS satellite, and meteorological data to find their impacts to predict dust storms temporal variations for the warm and cold months using various machine learning methods. The two studies were conducted in the semi-arid region of Iran, and the results show that an enhanced vegetation index impacts dust storm prediction during the warm months.…”

Section: Methods Used For Dust Storm Predictionsmentioning

confidence: 99%

Machine Learning Applications to Dust Storms: A Meta-Analysis

Alshammari

Alrwais

Aksoy

2022

Aerosol Air Qual. Res.

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Dust storms are natural hazards that affect both people and properties. Therefore, it is important to mitigate their risks by implementing an early notification system. Different methods are used to predict dust storms, such as observing satellite images, analyzing meteorological data, and using numerical weather prediction model forecasts. However, recent studies have shown that machine learning algorithms have higher capacities to predict dust storms in less time and with fewer processing operations compared to numerical weather models. This paper conducted a meta-analysis review to examine studies that addressed the areas associated with the application of machine learning to dust storm prediction. It aims to compare the applied models and the types of data used in the literature under study. Given that the location of a dust storm event is essential, the properties of dust storms are discussed in relation to the region. The output classes and the various performance metrics observed in each reviewed paper are also summarized. Subsequently, the present paper offers a detailed analysis highlighting the capabilities of machine learning models in predicting dust storms. The analysis shows two main categories: early detection and dust storm prediction. Most models used for dust storm early detection from satellite images are support vector machines (SVM). In contrast, the most used models for dust storm prediction are SVM and random forests that predict the occurrence of dust storms from meteorological data. Finally, the paper highlights the challenges and future trends in the field, illustrating the potential directions for applying deep learning algorithms and providing longrange predictions with assessments of dust storm duration and intensity.

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“…In consequence, fuzzy sets and MCDM methods are often used together [27]. Recent studies also consider the adaptive neuro-fuzzy inference system (ANFIS), which is a robust and capable model used for predictions, or risk factors assessment in contexts that generate interpretability [28]. ANFIS is functionally equivalent with Sugeno and Tsukamoto fuzzy system, but it has the capacity to adapt during a learning process [29].…”

Section: Risk Assessment -Brief Literature Overviewmentioning

confidence: 99%

Proactive risk assessment via fuzzy approach in a decisional process of consignment stock program adoption

FAUR

Bungău

2021

MATEC Web Conf.

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The idea of adopting the consignment stock concept has enriched the landscape of efficient supply chains and their organizations, due to its major benefits in reducing inventory, compressing delivery time and increasing flexibility towards achieving agility and enhanced market responsiveness. The decision making process is a complex one, as besides the benefits and the economical and administrative aspects, there are also risks that must be identified, measured, assessed and managed. There is little research in the literature concerning the risks and constraints of consignment inventory implementation, while consignment contracts are widely applied in both physical and virtual supply chains. This paper introduces a model of proactive risk assessment via a fuzzy approach, allowing a sensitivity analysis of the identified risks in the matrix, in terms of probability to happen, degree of severity, impact and potential consequences, as well as mitigation. A fuzzy inference system is used to serve as assessment instrument, to fairly and more rigorously evaluate the risks, in order to avoid critical situations during or after program adoption, or even implementation failure. Fuzzy logic theory has been chosen to capture the uncertainty that usually occurs when dealing with risks and decision making. We believe that having these risk assessment insights at hand, managers and practitioners can achieve a better understanding of the challenges that come along with a new consignment program adoption, while allowing them to make the right and justified decision, in accordance with both benefit and risk considerations.

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Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions

Cited by 24 publications

References 94 publications

New hybrid evolutionary models for spatial prediction of soil properties in Kurdistan

New hybrid evolutionary models for spatial prediction of soil properties in Kurdistan

Machine Learning Applications to Dust Storms: A Meta-Analysis

Proactive risk assessment via fuzzy approach in a decisional process of consignment stock program adoption

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