For the last 20 years, Istanbul has intensely experienced land use/cover change (LUCC) as a result of both the direction of investment strategies to the construction sector and urban transformation works. Within this process, the demand for the disposal of debris from building demolitions with minimum cost has made the creation of land reclamation areas a current issue. Land reclamation areas that pose a threat to the marine ecosystem also have effects on the local climate, depending on the LUCC experienced on the urban surface. In this study, two coastal reclamation areas of Istanbul (Yenikapı, Maltepe) were addressed, and the predictability of changes in the thermal environment after the landfill was examined using Artificial Neural Networks (ANN). When the relationship between the simulation data and the actual changes was statistically tested, correlations between the original and the simulated images of Maltepe and Yenikapı are 0.650 and 0.710 respectively were obtained. Also, it was determined that the simulations provided exact results in the range of 37–55%, and accurate results in the range of 66–87% with a sensitivity of 100 m. These results revealed that the simulations performed by the ANN have sufficient sensitivity for monitoring the thermal changes in urban areas.

在过去的 20 年里，由于建筑部门和城市改造工程的投资战略方向，伊斯坦布尔经历了强烈的土地利用/覆盖变化 (LUCC)。在这个过程中，对以最低成本处理建筑物拆除产生的碎片的需求使得土地复垦区的创建成为当前的问题。对海洋生态系统构成威胁的填海区也会对当地气候产生影响，这取决于城市表面的 LUCC。在这项研究中，伊斯坦布尔（Yenikapı、Maltepe）的两个沿海填海区得到了解决，并使用人工神经网络 (ANN) 检查了垃圾填埋场后热环境变化的可预测性。当对模拟数据与实际变化的关系进行统计检验时，Maltepe 和 Yenikapı 的原始图像和模拟图像之间的相关性分别为 0.650 和 0.710。此外，确定模拟提供了 37-55% 范围内的准确结果，以及 66-87% 范围内的准确结果，灵敏度为 100 m。这些结果表明，人工神经网络执行的模拟对于监测城市地区的热变化具有足够的敏感性。