Department of Disaster management, Begum Rokeya University, Rangpur, 5400, Bangladesh.
Research Scholars, Department of Geography, University of Gour Banga, Malda, India.
Environ Sci Pollut Res Int. 2021 Jul;28(26):34450-34471. doi: 10.1007/s11356-021-12806-z. Epub 2021 Mar 2.
Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and built-up area are the two most contributing drivers influencing the wetland risk assessment based on information gain ratio (InGR). The prediction results of machine learning models showed 64.48% of area by M5P, 61.75% of area by RF, 62.18% of area by REPTree, and 55.74% of area by SVM have been predicted as the high and very high-risk zones. The results of accuracy assessment showed that the RF outperformed than other models (area under curve: 0.83), followed by the SVM, M5P, and REPTree. Degradation of wetlands explored in this study demonstrated the negative effects on biodiversity. Therefore, to conserve and protect the wetlands, continuous monitoring of wetlands using high resolution satellite images, feeding with the ecological flow, confining built up area and agricultural expansion towards wetlands, and new wetland creation is essential for wetland management.
湿地风险评估是一个全球性的关注点，尤其是在孟加拉国等发展中国家。本研究探讨了湿地的时空动态变化，以及湿地风险评估的预测。湿地风险评估是基于十个选定的参数进行预测的，如破碎化概率、到道路的距离和定居点。我们使用 M5P、随机森林 (RF)、简化错误修剪树 (REPTree) 和支持向量机 (SVM) 机器学习技术进行湿地风险评估。结果表明，由于 Farakka 大坝的建设，目前的湿地面积减少了不到三分之一，而 Farakka 大坝位于 Padma 河的上游。基于信息增益比 (InGR)，到河流的距离和建成区是影响湿地风险评估的两个最重要的驱动因素。机器学习模型的预测结果表明，M5P 预测的高风险和极高风险区域占 64.48%，RF 预测的高风险和极高风险区域占 61.75%，REPTree 预测的高风险和极高风险区域占 62.18%，SVM 预测的高风险和极高风险区域占 55.74%。精度评估结果表明，RF 优于其他模型（曲线下面积：0.83），其次是 SVM、M5P 和 REPTree。本研究中探讨的湿地退化表明对生物多样性有负面影响。因此，为了保护湿地，需要使用高分辨率卫星图像对湿地进行持续监测，为湿地提供生态流量，限制建成区和农业向湿地的扩张，以及创造新的湿地，这对湿地管理至关重要。
