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Spatial and decadal prediction of land use/land cover using multi-layer perceptron-neural network (MLP-NN) algorithm for a semi-arid region of Asir, Saudi Arabia
Earth Science Informatics ( IF 2.7 ) Pub Date : 2021-06-24 , DOI: 10.1007/s12145-021-00633-2
Saeed Alqadhi , Javed Mallick , Akanksha Balha , Ahmed Bindajam , Chander Kumar Singh , Pham Viet Hoa

The present study uses Landsat satellite images of 1990, 2000 and 2018 to identify the land-use changes. Multilayer perceptron-neural network based land change modelling (LCM) has been applied to model future land-use/land cover (LULC). The prediction model has been validated using simulated and classified LULC maps of 2018 which resulted into an overall accuracy of 88%. The results indicate 389.27% increase in built-up area as the prominent land-use change during 1990–2018 and an increase of 56.25% in built-up area is forecasted during the year 2018–2040. Land absorption coefficient and land consumption rate indices, used to characterize urban expansion, indicate continued compact built-up structure during 1990–2018 due to population increase. The observations derived from this study would be useful as it will help the regional planners with forecasted land-use beforehand in planning the built-up and abundantly available natural resources in the area according to the increasing future demands.



中文翻译:

使用多层感知器-神经网络 (MLP-NN) 算法对沙特阿拉伯阿西尔半干旱地区的土地利用/土地覆盖进行空间和年代际预测

本研究使用 1990、2000 和 2018 年的 Landsat 卫星图像来识别土地利用变化。基于多层感知器神经网络的土地变化建模 (LCM) 已被应用于对未来土地利用/土地覆盖 (LULC) 进行建模。该预测模型已使用 2018 年模拟和分类的 LULC 地图进行了验证,总体准确度为 88%。结果表明,1990-2018年土地利用变化突出,建成区面积增加389.27%,预计2018-2040年建成区面积增加56.25%。用于表征城市扩张的土地吸收系数和土地消耗率指数表明 1990-2018 年期间由于人口增加,建筑结构持续紧凑。

更新日期:2021-06-24
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