The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, NSW, Australia; Natural Resources Management Centre, Department of Agriculture, Peradeniya 20400, Sri Lanka.
The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, NSW, Australia; Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea; Earth Observation Center, Institute of Climate Change, University Kebangsaan Malaysia, 43600, UKM, Bangi, Selangor, Malaysia.
Sci Total Environ. 2022 Nov 1;845:157220. doi: 10.1016/j.scitotenv.2022.157220. Epub 2022 Jul 12.
Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability.
降雨变化导致世界各地频繁发生意外灾害。降雨强度的增加显著加剧了土壤侵蚀和与土壤侵蚀相关的危害。准确预测降雨有助于及早发现土壤侵蚀的脆弱性，并通过采取适当措施，最大限度地减少强风暴、干旱和洪水造成的破坏。本研究旨在使用深度学习方法：长短期记忆神经网络模型（LSTM）和修正的通用土壤流失方程（RUSLE）模型来预测土壤侵蚀概率。从 1990 年到 2021 年，从斯里兰卡中央高地的五个农业气象站收集了每日降雨数据，并将其输入到 LSTM 模型模拟中。使用地理信息工具创建了 2024 年的降雨侵蚀力图层。LSTM 模型以长时滞预测时间序列月降雨数据，预测每个站未来 36 个月的降雨值。RUSLE 模型预测显示，高地的年平均土壤侵蚀量将为 11.92 吨/公顷/年。土壤侵蚀易感性图表明，约 30%的土地面积将被归类为中度到高度易受土壤侵蚀的类别。生成的地图层使用为 2000 年、2010 年和 2019 年开发的过去的土壤侵蚀地图层进行了验证。土壤侵蚀易感性图的准确率为 0.93，接收者操作特征曲线下的面积（AUC-ROC），表明预测性能令人满意。这些发现将有助于在政策层面做出决策，研究人员可以进一步用 RUSLE 模型测试不同的深度学习模型，以提高土壤侵蚀概率的预测能力。
