Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models

Bazrafshan, Ommolbanin; Ehteram, Mohammad; Latif, Sarmad Dashti; Huang, Yuk Feng; Teo, Fang Yenn; Ahmed, Ali Najah; El‐Shafie, Ahmed

doi:10.1016/j.asej.2022.101724

Cited by 39 publications

(12 citation statements)

References 52 publications

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“…Calculate the total score of the scheme Score i according to formula (3) ( 5) End (6) Sort P ( , P.Score);//Arrange the policy set in descending order by each goal score (7) For i � 1 to P.size (8) For j � 1 to number of harmony test indicators (9) If the j-th harmony test index of strategy I is less than its j-th harmony test index threshold (10) en reject this scheme; (11) End (12) For j � 1 to threshold number of objective function (13) If P.score j <j-th objective function threshold (14) en reject this scheme; (15) End ( 16) End ( 17) Print Pi//output the optimal policy ALGORITHM 2: Multi-index ranking method.…”

Section: Test Of Adaptive Decision Methods For Command Andmentioning

confidence: 99%

“…At present, search-based software engineering has become a research hotspots. However, the research on the application of this theory to adaptive software is still in the exploratory stage [13], and the main work focuses on the framework design [14], architecture optimization [15], software testing [16], development process and workload estimation [17], programming and repair [18] of adaptive software systems. For example, the Moses framework is an adaptive system optimization framework for service-oriented computing [19], and the research on the combination of adaptive software architecture and search-based software engineering proposed by Ref.…”

Section: Search-based Adaptive Decisionmentioning

confidence: 99%

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Adaptive Decision Method in C3I System

Gao

Wang

Nazarko

et al. 2022

Computational Intelligence and Neuroscience

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As an important system in the national defense and military information construction, the command, control, communication, and intelligence (C3I) system urgently needs to establish an adaptive process to deal with the dynamic operating environment and changeable task requirements to ensure the long-term effective and stable operation of the system. As an important part of this process, the adaptive decision method should have the ability of online trade-off decision. Therefore, this paper presents an adaptive decision method based on parallel computing and optimization theory. This method combines operational requirements and commander preference to achieve the parallel adaptive decision solution. The experimental results show that the presented decision method can generate online trade-off strategies to deal with typical command and control scenarios of damage replacement in a simulated environment, effectively guide the system to carry out adjustment behavior, and achieve the goal of dynamic response to environmental changes and task changes.

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Section: Test Of Adaptive Decision Methods For Command Andmentioning

confidence: 99%

Section: Search-based Adaptive Decisionmentioning

confidence: 99%

Adaptive Decision Method in C3I System

Gao

Wang

Nazarko

et al. 2022

Computational Intelligence and Neuroscience

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“…The detail of the CLSTM theory has been introduced in [14][15][16][17] for successfully predicting various datasets including solar photosynthetic photon ux density, solar radiation, photovoltaic power, and thermal displacement. Additionally, the studies in [18][19][20][21][22][23] present the mathematical formulas of the other deep learning models (LSTM, CNN, DNN, MLP) used in this study. On the other hand, the theoretical backgrounds of ELM, RFR and XGB as the machine learning models are clearly described in [24][25][26][27] and the Pro6UV Deterministic model has been explained in detail in [9].…”

Section: Theoretical Overviewmentioning

confidence: 99%

Multi-step solar UV index prediction using deep learning methods

AL‐Musaylh

Ghimire

Al‐Daffaie

et al. 2023

Preprint

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The impact of ultraviolet (UV) radiation on public health is severe and can cause sunburn, skin aging and cancer, immunosuppression, and eye damage. Minimization of exposure to solar UV is required in order to reduce the risks of these illnesses to the public. Greater public awareness and a prediction of the ultraviolet index (UVI) is considered an essential task for the minimization of solar UV exposures. This research has designed artificial intelligence based deep learning models to predict multistep UVI index. It has developed a convolutional neural network integrated with long short-term memory network (CLSTM) as the main model to forecast UVI for Brisbane with latitude − 27.47 and longitude 153.02, the capital city of Queensland, Australia. Solar zenith angle (SZA) data were used together with UVI as inputs in the CLSTM for 10-min, 30-min and 60-min UVI prediction. The CLSTM model was benchmarked against long short-term memory network (LSTM), convolutional neural network (CNN), Deep Neural Network (DNN), multilayer perceptron (MLP), extreme learning machine (ELM), random forest regression (RFR), Extreme Gradient Boosting (XGB), and Pro6UV Deterministic models. The experimental results showed that the CLSTM model outperformed these models with RMSE = 0.3817, MAE = 0.1887, RRMSE = 8.0086%, MAPE = 4.6172% and APB = 3.9586 for 10-min prediction. In addition to that, these metrics for 30-min and 60-min prediction were RMSE = 0.4866/0.5146, MAE = 0.2763/0.3038, RRMSE = 10.4860%/11.5840%, MAPE = 8.1037%/9.6558% and APB = 5.9546/6.8386, respectively. Thus, the CLSTM model can yield improved UVI prediction for both the public and the government agencies.

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“…However, advanced computational technologies, such as machine learning, can increase the capability and accuracy of model prediction (Muruganantham et al., 2022; Shahhossini et al., 2020). In recent years, a hybrid modeling approach has been proposed by many scientists (Bazrafshan et al., 2022; S. Kim & Kim, 2022; Kim et al., 2018a; Oikonomidis et al., 2022; Shahhosseini et al., 2021). The hybrid modeling approach is a method of using different modeling techniques in the same workflow (Mykoniatis & Harris, 2021).…”

Section: Introductionmentioning

confidence: 99%

A hybrid simulation framework for predicting virus impacts on Chinese cabbage yields

Kyoung,

Kim,

Kim

2023

Agronomy Journal

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Sustainable production of Chinese cabbage (Brassica rapa L.) is essential for the economy and food security in South Korea. However, climate change poses significant threats to its growth and quality. Changes in precipitation patterns such as drought and heavy rain can affect the development of crop, leading to stunted growth or disease. This can lead to significant losses in crop yield. In this study, a hybrid modeling system was developed to predict incidence of viral disease and evaluate impacts of combination of virus infection and environmental variabilities (including climate and location) on marketable cabbage yields. The crop growth model was successfully developed using a limited number (n = 7) of previous studies (RMSE 0.25‐0.33 Mg ha−1, R2 = 0.87‐0.99). The developed hybrid modeling system was composed of a virus incident model and a crop growth model. According to simulated results, all study locations had around 20% incidence rates. However, based on the simulation results, the rates of viral disease incidence varied depending on the climate of each year. Among several climatic factors, precipitation had the greatest effect on virus outbreaks. Jeju Island, which had a relatively high rainfall, had a higher disease incidence rate than other provinces. According to shared socioeconomic pathways (SSPs), in SSP245 (an intermediate development pathway), the yield of 1.58 Mg ha−1 was reduced to 1.26 Mg ha−1 due to a 25% viral disease outbreak. In SSP585 (a high development pathway), with an incidence of 23%, the yield was reduced by 0.2 Mg ha−1. These results will be useful for efficiently cultivating crops under various climatic conditions and seeking management methods to minimize damage from viral disease.This article is protected by copyright. All rights reserved

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Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models

Cited by 39 publications

References 52 publications

Adaptive Decision Method in C3I System

Adaptive Decision Method in C3I System

Multi-step solar UV index prediction using deep learning methods

A hybrid simulation framework for predicting virus impacts on Chinese cabbage yields

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