Integration of Markov chain analysis and similarity-weighted instance-based machine learning algorithm (SimWeight) to simulate urban expansion
Integration of Markov chain analysis and similarity-weighted instance-based machine learning algorithm (SimWeight) to simulate urban expansion
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DOI：
10.1080/12265934.2017.1284607
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发表时间：
2017-01-01
影响因子：
2.9
通讯作者：
Bununu, Yakubu Aliyu
中科院分区：
文献类型：
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作者：
Bununu, Yakubu Aliyu
本研究以尼日利亚西北部的卡杜纳为例，模拟城市扩张。为了克服元胞自动机、马尔可夫链和标准Logistic回归模型等已知建模技术的缺陷，提出了一种混合模型，该模型集成了基于实例的相似加权机器学习算法和马尔可夫链模型来量化和分配土地利用变化。对城市扩展起制约和(或)激励作用的环境和城市实际变数被付诸实施，以创造1990年和2001年建成土地利用时空状态的过渡潜力。利用一致性统计的相对运行特性和Kappa指数对模型进行了评价和验证。在验证过程中获得了令人满意的结果后，所建模的转型潜力被用于预测未来几年的城市扩展。模拟的土地利用地图为卡杜纳在可预见的未来可能发生的城市扩张的位置和类型提供了宝贵的见解。这为城市管理者和规划者提供了急需的信息，这些信息可以为旨在更好地规划和管理城市发展的城市政策提供信息。
This study simulates urban expansion using Kaduna in North-West Nigeria as a case study. A hybrid model that integrates the similarity-weighted instance-based machine learning algorithm for transition potential modelling and the Markov chain model to quantify and allocate land-use change was used to overcome the identified weaknesses of known modelling techniques such as the cellular automata, Markov chain and standard logistic regression models. Environmental and urban physical variables that act as constraints and/or incentives to urban expansion were operationalized to create transition potentials for spatiotemporal states of built-up land use for the year 1990 and 2001. Model evaluation and validation was carried out using the relative operating characteristic and kappa index of agreement statistics. Having obtained satisfactory outcomes from the validation process, the modelled transition potentials were used to predict future urban expansion for forthcoming years. The simulated land-use maps provide valuable insights into the location and type of urban expansion that is likely to occur in Kaduna in the foreseeable future. This provides city managers and planners much needed information that could inform urban policy aimed at better planning and management of urban development.