Capabilities of deep-learning models CNN and ConvLSTM are explored for analyzing and projecting future urban growth in four Indian coastal cities, viz., Mumbai, Chennai, Kochi, and Vishakhapatnam. ConvLSTM performed better with higher overall accuracy, Cohen's kappa, macro F1-score (> 95 %), and R2 and NSE values (> 0.87). The urbanization trends indicate a higher growth in Kochi (∼90.5 %), and greater projected rate for Mumbai (∼42.5 %). The monthly accumulated rainfall and mean temperature are analyzed and forecasted through the ConvLSTM model. The Mann-Kendall test-based analysis (with p-value <0.05), suggest no significant rainfall pattern for the cities except Mumbai, which exhibits an increasing trend. The projected rainfall trend is unaltered except for Vishakhapatnam, which is expected to increase in the coming years. The mean temperature over all the cities, shows an increasing trend (slope ∼ 0.02). However, in the coming years, the ‘increasing trend’ is expected to change into a ‘no significant trend’ for Mumbai and Vishakhapatnam. The forecasted results indicate a continuous urban expansion, which is expected to have a significant impact on rainfall and temperature trends. The outcome could assist urban planners in defining the timeline for improving drainage and include green spaces in the city development plans.

中文翻译：

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使用深度学习模型分析印度四个沿海城市的城市足迹以及与降雨量和温度的关联


探索了深度学习模型 CNN 和 ConvLSTM 的功能，用于分析和预测印度四个沿海城市（孟买、金奈、科钦和维沙卡帕特南）未来的城市增长。 ConvLSTM 表现更好，具有更高的整体准确度、Cohen 的 kappa、宏观 F1 分数 (> 95 %) 以及 R2 和 NSE 值 (> 0.87)。城市化趋势表明，高知的增长速度较高（约 90.5%），而孟买的预计增长速度较高（约 42.5%）。通过ConvLSTM模型对月累计降雨量和平均气温进行分析和预测。基于 Mann-Kendall 测试的分析（p 值为 <0.05）表明，除孟买外，其他城市没有显着的降雨模式，孟买呈现出增加的趋势。除维沙卡帕特南外，预计降雨趋势保持不变，预计未来几年降雨量将增加。所有城市的平均气温均呈上升趋势（斜率∼0.02）。然而，未来几年，孟买和维沙卡帕特南的“增长趋势”预计将转变为“无显着趋势”。预测结果表明城市持续扩张，预计将对降雨和气温趋势产生重大影响。研究结果可以帮助城市规划者确定改善排水的时间表，并将绿色空间纳入城市发展计划。