Gholami Hamid, Mohammadifar Aliakbar, Golzari Shahram, Song Yougui, Pradhan Biswajeet
Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
Department of Electrical and Computer Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran; Deep Learning Research Group, University of Hormozgan, Bandar Abbas, Hormozgan, Iran.
Sci Total Environ. 2023 Dec 15;904:166960. doi: 10.1016/j.scitotenv.2023.166960. Epub 2023 Sep 9.
Gully erosion possess a serious hazard to critical resources such as soil, water, and vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be instrumental in mitigating its negative consequences. Among the various methods used to explore and map gully erosion, advanced learning techniques, especially deep learning (DL) models, are highly capable of spatial mapping and can provide accurate predictions for generating spatial maps of gully erosion at different scales (e.g., local, regional, continental, and global). In this paper, we applied two DL models, namely a simple recurrent neural network (RNN) and a gated recurrent unit (GRU), to map land susceptibility to gully erosion in the Shamil-Minab plain, Hormozgan province, southern Iran. To address the inherent black box nature of DL models, we applied three novel interpretability methods consisting of SHaply Additive explanation (SHAP), ceteris paribus and partial dependence (CP-PD) profiles and permutation feature importance (PFI). Using the Boruta algorithm, we identified seven important features that control gully erosion: soil bulk density, clay content, elevation, land use type, vegetation cover, sand content, and silt content. These features, along with an inventory map of gully erosion (based on a 70 % training dataset and 30 % test dataset), were used to generate spatial maps of gully erosion using DL models. According to the Kolmogorov-Smirnov (KS) statistic performance assessment measure, the simple RNN model (with KS = 91.6) outperformed the GRU model (with KS = 66.6). Based on the results from the simple RNN model, 7.4 %, 14.5 %, 18.9 %, 31.2 % and 28 % of total area of the plain were classified as very-low, low, moderate, high and very-high hazard classes, respectively. According to SHAP plots, CP-PD profiles, and PFI measures, soil silt content, vegetation cover (NDVI) and land use type had the highest impact on the model's output. Overall, the DL modelling techniques and interpretation methods used in this study proved to be helpful in generating spatial maps of soil erosion hazard, especially gully erosion. Their interpretability can support watershed sustainable management.
沟蚀对流域内的土壤、水和植被覆盖等关键资源构成严重威胁。因此，沟蚀危害的空间地图有助于减轻其负面影响。在用于探索和绘制沟蚀地图的各种方法中，先进的学习技术，特别是深度学习（DL）模型，具有很强的空间制图能力，能够为不同尺度（如局部、区域、大陆和全球）生成沟蚀空间地图提供准确预测。在本文中，我们应用了两种深度学习模型，即简单循环神经网络（RNN）和门控循环单元（GRU），来绘制伊朗南部霍尔木兹甘省沙米尔 - 米纳布平原的沟壑侵蚀易发性地图。为了解决深度学习模型固有的黑箱性质，我们应用了三种新颖的可解释性方法，包括SHapley加法解释（SHAP）、条件不变和局部依赖（CP - PD）剖面以及排列特征重要性（PFI）。使用博鲁塔算法，我们确定了七个控制沟蚀的重要特征：土壤容重、粘土含量、海拔、土地利用类型、植被覆盖、砂含量和粉砂含量。这些特征以及沟蚀清单地图（基于70%的训练数据集和30%的测试数据集）被用于使用深度学习模型生成沟蚀空间地图。根据柯尔莫哥洛夫 - 斯米尔诺夫（KS）统计性能评估指标，简单RNN模型（KS = 91.6）优于GRU模型（KS = 66.6）。基于简单RNN模型的结果，平原总面积的7.4%、14.5%、18.9%、31.2%和28%分别被归类为极低、低、中、高和极高危害等级。根据SHAP图、CP - PD剖面和PFI度量，土壤粉砂含量、植被覆盖（归一化植被指数）和土地利用类型对模型输出的影响最大。总体而言，本研究中使用的深度学习建模技术和解释方法被证明有助于生成土壤侵蚀危害的空间地图，特别是沟蚀地图。它们的可解释性可以支持流域的可持续管理。
