Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model.
Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model.
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DOI：
10.1016/j.heliyon.2020.e05092
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发表时间：
2020-09
期刊：
影响因子：
4
通讯作者：
Al-Kofahi S
中科院分区：
文献类型：
--
作者：
Gharaibeh A;Shaamala A;Obeidat R;Al-Kofahi S
城市增长和土地使用变化是影响我们未来城市的许多令人困惑的因素中的几个。为未来的土地变化创建精确的模拟是一个具有挑战性的过程，需要时间和空间建模。最近的许多研究开发和训练了使用人工智能（AI）预测城市扩张模式的模型。本研究旨在提高元胞自动机马尔可夫链模型（CA-MC）在预测土地利用变化方面的模拟能力。本研究整合了人工神经网络（ANN）到CA-MC纳入几个驱动力，高度影响土地利用变化。该研究利用不同的社会经济，空间和环境变量（坡度，道路距离，城市中心距离，商业距离，密度，海拔和土地肥力），使用ANN数据驱动模型生成潜在的过渡图。生成的映射作为附加输入被馈送到CA-MC。我们校准了原始的CA-MC和我们的模型，以交叉比较2015年约旦伊尔比德市的模拟地图和实际地图。我们的模型的验证进行了评估，并使用Kappa指数，包括协议的数量和位置的CA-MC模型进行比较。结果表明，我们的模型的准确率为90.04%，大大优于CA-MC模型（86.29%）。通过将人工神经网络与CA-MC相结合，我们得到的改进表明，为了更准确地预测，需要考虑驱动力的影响。除了改进的模型预测外，伊尔比德2021年和2027年的预测地图将指导地方当局制定平衡城市扩张和保护农业地区的管理战略。这将在维持约旦的粮食安全方面发挥至关重要的作用。环境科学、计算机科学、地理学、土地利用规划、土地利用变化、城市增长、机器学习、城市规划、建模;人工智能、元胞自动机、马尔可夫链。
Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security. Environmental science, Computer science, Geography, Land use planning, Land use change, Urban growth, Machine learning, Urban planning, Modeling; Artificial intelligence, Cellular Automata, Markov Chain.
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1016/j.jag.2017.03.006
发表时间：
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