Analyzing and modeling of a large river basin dynamics applying integrated cellular automata and Markov model

Pandey, Brij Kishor; Khare, Deepak

doi:10.1007/s12665-017-7133-4

Cited by 24 publications

(7 citation statements)

References 41 publications

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“…For this reason, systems that manage to predict these long-term consequences will be very useful. This proposition has been presented in different investigations such as [87,[106][107][108][109][110][111][112][113][114][115][116][117][118][119] where agent-based models were used; likewise, the research of [92,96,[120][121][122][123][124][125] employed the technique of cellular automaton.…”

Section: Discussionmentioning

confidence: 99%

“…Besides, this research applied artificial neural networks [92]. Other research sought to determine changes in land use due to the expansion of agriculture, environmental calamities, and government policies on the Upper Narmada-India basin [120]. Researchers simulated the conflict between the protection of agricultural land and urban growth in China and the ability to ensure food security in the region.…”

Section: Cellular Automatonmentioning

confidence: 99%

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Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

2020

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The objective of this article is to review how Artificial Intelligence (AI) tools have helped the process of formulating agricultural public policies in the world. For this, a search process was carried out in the main scientific repositories finding different publications. The findings have shown that, first, the most commonly used AI tools are agent-based models, cellular automata, and genetic algorithms. Secondly, they have been utilized to determine land and water use, and agricultural production. In the end, the large usefulness that AI tools have in the process of formulating agricultural public policies is concluded.

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Section: Discussionmentioning

confidence: 99%

Section: Cellular Automatonmentioning

confidence: 99%

Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

2020

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“…The use of these techniques for geographic process modeling dates back to the nineties. Advanced CA models currently exist for modeling and simulating spatial dynamics, evacuation systems, river basin dynamics, forest fire spreading [11] [12] [13] [14]. In particular, these cellular techniques have also contributed to the modeling and simulation of residential mobility.…”

Section: Cellular Automata Approachmentioning

confidence: 99%

An Agent Cellular Residential Mobility Model : From Functional and Conceptual View

El-alaouy¹,

Rhoulami²,

Rahmani³

2018

ijacsa

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Residential mobility is of great challenge to sustainable cities. Developing computer models based simulation could be a powerful tool to support informing urban decisions especially with the fact that half of the world's population now lives in cities. The present paper presents our detailed model of residential mobility which use an alliance Multi-agent systems and Cellular automata (MAS-CA) approach. Conventional Urban modelling approaches will be presented firstly with a distinct light sheded on the alliance CA-MAS approach. The model will be then exposed in its two functional and conceptual views. At the end, results of a scenario of growth population is simulated and discussed. Results of the simulation shows significant conformity with the underlying model hypothesis.

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“…Pandey uses CA and the Markov model to simulate a large river basin and to help protect natural vegetation and the management of the built-up area [18]. An artificial neural network (ANN), which is integrated with the CA-Markov model to optimize and allocate urban expansion cells, shows more satisfactory results in urban areas [19]. Because the neural network algorithm has non-linear characteristics in simulating the conversion rules of different land-use types, it has a wide range of applications to simulate land use [20].…”

Section: Introductionmentioning

confidence: 99%

Future Flood Risk Assessment under the Effects of Land Use and Climate Change in the Tiaoxi Basin

Yang

2020

Sensors

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Global warming and land-use change affects runoff in the regional basin. Affected by different factors, such as abundant rainfall and increased impervious surface, the Taihu basin becomes more vulnerable to floods. As a result, a future flood risk analysis is of great significance. This paper simulated the land-use expansion and analyzed the surface change from 2020 to 2050 using the neural network Cellular Automata Markov (CA-Markov) model. Moreover, the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) dataset was corrected for deviation and used to analyze the climate trend. Second, the verified SWAT model was applied to simulate future runoff and to analyze the future flood risk. The results show that (1) land use is dominated by cultivated land and forests. In the future, the area of cultivated land will decrease and construction land will expand to 1.5 times its present size. (2) The average annual precipitation and temperature will increase by 1.2% and 1.5 degrees from 2020 to 2050, respectively. During the verified period, the NSE and r-square values of the SWAT model are greater than 0.7. (3) Compared with the historical extreme runoff, the extreme runoff in the return period will increase 10%~25% under the eight climate models in 2050. In general, the flood risk will increase further under the climate scenarios.

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Analyzing and modeling of a large river basin dynamics applying integrated cellular automata and Markov model

Cited by 24 publications

References 41 publications

Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

An Agent Cellular Residential Mobility Model : From Functional and Conceptual View

Future Flood Risk Assessment under the Effects of Land Use and Climate Change in the Tiaoxi Basin

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