Assessing subsidence susceptibility to coal mining using frequency ratio, statistical index and Mamdani fuzzy models: evidence from Raniganj coalfield, India
2020
Rehman, Sufia | Sahana, Mehebub | Dutta, Shyamal | Sajjad, Haroon | Song, Xuang | Imdad, Kashif | Dou, Jie
Raniganj, an important coalfield in India, is susceptible to mining subsidence, ejection of toxicants in the environment and huge subsurface destruction. Mining with opencast method poses a great risk to the surrounding areas of this coalfield. Anthropogenic and natural activities have caused ground movement leading to subsidence in Raniganj coalfield. Existing knowledge on subsidence susceptibility to coal mining in Raniganj coalfield is scant. The study makes an attempt to analyze mining subsidence susceptibility in Raniganj coalfield using site-specific parameters. We first prepared subsidence inventory map to identify old subsidence locations and to construct the model. It was later utilized for validating the models. Fifteen site-specific parameters related to subsidence were chosen to analyze coal mining subsidence. Frequency ratio (Fr), statistical index (SI) and Mamdani fuzzy (Mf) models were utilized to assess their effectiveness in preparing susceptibility map. Findings of the study revealed very high susceptibility in the central part, high susceptibility in north-western part and moderate susceptibility in the eastern part of Raniganj coalfield. Low and very low susceptibility was found in those parts having largest area under vegetation and water bodies. The models were validated through ROC curve, seed cell area index (SCAI) and spatially agreed area approach. Mamdani fuzzy model with highest success rate (87%) and prediction accuracy (85%) was found the best fit model for analyzing mining subsidence susceptibility. The framework of the methodology will be instructive for analyzing susceptibility at different geographical locations.
Показать больше [+] Меньше [-]Ключевые слова АГРОВОК
Библиографическая информация
Эту запись предоставил National Agricultural Library