A multi-state Markov chain model to assess drought risks in rainfed agriculture: a case study in the Nineveh Plains of Northern Iraq

Fadhil, Rasha M.; Unami, Koichi

doi:10.1007/s00477-021-01991-5

Cited by 13 publications

(5 citation statements)

References 44 publications

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“…It can be seen from the algorithm flow that the algorithm includes two layers of loops. The inner loop represents the random search of the state at the same temperature, while the outer loop is the change when the temperature drops and the algorithm stops conditions [ 38 ]. According to this characteristic, SA can help the neural network jump out of local optimum.…”

Section: Construction Of the Obesity Monitoring Model Based On Sa Algorithmmentioning

confidence: 99%

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Ren

Zhao

2021

Computational Intelligence and Neuroscience

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With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.

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Section: Construction Of the Obesity Monitoring Model Based On Sa Algorithmmentioning

confidence: 99%

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Ren

Zhao

2021

Computational Intelligence and Neuroscience

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“…Several researches have been done in Iran on drought monitoring and forecasting using drought indicators and Markov chain model; Among other things, in a study in the Salt Lake basin, which was conducted using several indices, including the decimal index, the normal percentage index, and the standard bar 1 index, it was shown that the standard precipitation index is the most suitable index for evaluating the severity of drought ( Fadhil et al 2021) and (Guo et al…”

Section: )mentioning

confidence: 99%

Analysis and forecasting drought characterisation with using Markov chain model

Javadinejad¹,

Dara²,

Krause

et al. 2023

Saf. Extreme Environ.

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Drought is one of the inseparable parts of climate uctuations that cause a lot of damage every year. Considering the effects of drought on different parts of the environment, agriculture, natural resources, wildlife, etc., its prediction can be useful for managing the crisis and reducing the damages caused by it. In the current research, monthly drought was calculated based on the standard precipitation index in several stations in the south of Iran during the years 1980-2020; Then, using the Markov chain, monthly drought was predicted for the years 2020 to 2040. According to the results, most of the stations have normal, moderate and severe drought conditions.The transition probability matrix showed that in all stations, the probability of passing from a certain state to the same state and the probability of passing from wet to dry state is high; But the probability of transition from dry to wet is low. Also, the predictive results were measured at different stations with different levels of accuracy.In addition, the results showed that the highest probability of drought in the years 2020-2040 is related to normal, moderate and severe classes, and at the level of the studied area, from class one to seven, the 13.4, 26.81, 27,74, 37.11, 4.76, 2.88, and 0.70% of the predicted months drought will happen respectively.

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“…For the verification of the empirical distribution with the theoretical distribution, nonparametric consistency tests are commonly used [52][53][54]. The choice of test depends on the type of distribution being verified, the random variable and the sample size.…”

Section: Statistical Analysis Of Datamentioning

confidence: 99%

Analysis of Light Utility Vehicle Readiness in Military Transportation Systems Using Markov and Semi-Markov Processes

2022

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This paper presents the issues of modeling the operation process of light utility vehicles operating in military transport systems. The required condition for the effective operation of the system is to maintain the means of transport at the appropriate level of technical readiness. For this purpose, it is necessary to equip the technical system with appropriate resources enabling the efficient implementation of fuel refilling, maintenance and repair processes. Each failure of the means of transport causes a significant reduction in transport capacity, which then results in the inability to perform the planned tasks. Quality control and vehicle operation process management require advanced mathematical methods and tools. Three indicators have been proposed as quantitative characteristics for assessing and optimizing the availability of military vehicles: functional readiness, technical efficiency and airworthiness. To determine their value, a stochastic exploitation model was developed based on the application of the theory of Markov processes. Based on the collected empirical data, a nine-state phase space of the studied process was identified. Operating states were distinguished relating to the implementation of the transport task, refueling, parking in the garage, as well as maintenance and repairs. As part of the considerations for the continuous time, verification of the distributions of time characteristics led to the development of a semi-Markov model. The ergodic probabilities calculated based on the conditional probability matrix of interstate transitions and the expected values of the time spent in the states were used to determine the indicators of functional availability, efficiency and technical suitability. In order to determine the possibility of optimizing the process, a sensitivity analysis was performed. Reducing the amount of time the vehicles must wait for repair by about 50% can improve the values of the indexes from 0.91 to 0.95.

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A multi-state Markov chain model to assess drought risks in rainfed agriculture: a case study in the Nineveh Plains of Northern Iraq

Cited by 13 publications

References 44 publications

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Analysis and forecasting drought characterisation with using Markov chain model

Analysis of Light Utility Vehicle Readiness in Military Transportation Systems Using Markov and Semi-Markov Processes

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