Cellular automata approach for modelling climate change impact on water resources

Kassogué, Hamidou; Bernoussi, Abdes Samed; Amharref, Mina; Ouardouz, Mustapha

doi:10.1080/17445760.2017.1331438

Cited by 6 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When encountering heavy rainfall, the underground dark river water pours into the mining area in large quantities and rapidly, causing the water level of the mine to rise and destroying the hydraulic relationship between the overlying aquifer and the mine water. By constructing drainage boreholes in the mining area and emergency pumping out treatment to control the mine water level rising too fast, keep the hydraulic relationship between the overlying aquifer and mine water basically unchanged, control the polluted water overflow, and realize the river water body not being polluted under extreme weather conditions [14][15].…”

Section: Emergency Response Pumping Treatment Technology Based On "U-...mentioning

confidence: 99%

Fusion Chaos Neural Network Algorithm to Control Water Pollution Prevention in Mine Reconstruction Projects

2022

WPPCP

View full text Add to dashboard Cite

In recent years, the mining and utilization of large-scale mineral resources and the promotion of mine reconstruction projects have led to the increasingly serious problems of heavy metal pollution and water pollution (WP) in mine waters, which have posed potential threats to the health of the residents in and around the mines. Therefore, it is of vital importance to study the pollution characteristics of water bodies in mining areas to improve the water environment in mining areas. In this study, three mine alterations are used as the research objects to initially study the current WP status of surface water and groundwater in the watersheds near the mines, and to make a preliminary evaluation of the water environment quality using chaotic neural network algorithm (CNNA), so as to clarify the WP situation under the influence of mine alterations. This study has some application value for the practice of WP prevention and control engineering in mine reconstruction projects.

show abstract

Section: Emergency Response Pumping Treatment Technology Based On "U-...mentioning

confidence: 99%

Fusion Chaos Neural Network Algorithm to Control Water Pollution Prevention in Mine Reconstruction Projects

2022

WPPCP

View full text Add to dashboard Cite

show abstract

“…The prevention and planning of WP prevention is an important part of environmental planning, which is inseparable from the economic development of cities. Therefore, governments at all levels should follow four planning principles: first, environmental protection must be coordinated with the local economy; Second, protect the environment and monitor natural laws; Third, prevention and control of WP; Fourth, plan the time to prevent and control WP, and actively adopt different methods to improve the possibility of environmental protection according to local environmental problems [10][11].…”

Section: Wp Preventionmentioning

confidence: 99%

Urban Water Pollution Prevention and Protection Based on Convolutional Neural Network

2022

WPPCP

View full text Add to dashboard Cite

Although China's urbanization is developing rapidly and people's quality of life has been greatly improved, the water environment of people's life is getting worse and worse. Water pollution (WP) has become a major environmental problem endangering people's daily life. Water is an indispensable resource in people's daily life. People should cherish our limited water resources and strengthen the protection of water resources. Therefore, this paper deeply analyzes the current situation of WP in A city, and combines convolutional neural network (CNN) to simulate the morphological characteristics of pollutants in water. Through this study, it is found that the industrial pollution in City A has the greatest impact on the water environment. The industrial pollutants are directly discharged into the river basin, resulting in an increase in the content of COD, ammonia nitrogen and TP in the water body. In addition, there are problems in the prevention and control of WP in City A, such as weak government supervision, weak prevention and control willingness, and low participation of citizens in the prevention and control of WP. Therefore, this paper puts forward corresponding water resources protection suggestions, I hope it can provide reference for WP control in other cities.

show abstract

“…The von Neumann neighbourhood and Moore neighbourhood transition systems consist of four and eight surrounding cells respectively (Kumar & Sharma, 2019;Hu & Cai, 2020;Praba & Saranya, 2020). The CA transition rules are predefined conditions that determine the future state of every cell in the lattice (Kassogué et al, 2019;Mohamed & Worku, 2020). These rules must be fulfilled before any cell transition is possible (Sakieh et al, 2015;Liu et al, 2018;Jetto et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Integrating machine learning with Markov chain and cellular automata models for modelling urban land use change

Okwuashi

Ndehedehe

2021

Remote Sensing Applications: Society and Environment

View full text Add to dashboard Cite

Cellular automata approach for modelling climate change impact on water resources

Cited by 6 publications

References 7 publications

Fusion Chaos Neural Network Algorithm to Control Water Pollution Prevention in Mine Reconstruction Projects

Fusion Chaos Neural Network Algorithm to Control Water Pollution Prevention in Mine Reconstruction Projects

Urban Water Pollution Prevention and Protection Based on Convolutional Neural Network

Integrating machine learning with Markov chain and cellular automata models for modelling urban land use change

Contact Info

Product

Resources

About