Search

Landslides are frequent geological hazards, mainly in the rainy season along road corridors worldwide. In the present study, we have comparatively analyzed landslide susceptibility by employing integrated geospatial approaches, i.e., data-driven, knowledge-driven, and machine learning (ML), along the main road corridors of the Muzaffarabad district. The landslide inventory of three road corridors is developed to evaluate landslide susceptibility, and eleven landslide causative factors (LCFs) were analyzed. After statistical significance analysis, these eleven LCFs generated susceptibility models using WoE, AHP, LR, and RF. Distance from roads, landcover, lithological units, and slopes are considered more influential LCFs. The performance matrix of different LSMs is evaluated through the area under the curve (AUC-ROC), overall accuracy, Kappa index, F1 score, Mean Absolute Error, and Root Mean Square Error. The AUC-ROC for WoE, AHP, LR, and RF techniques along Neelum road is 0.86, 0.82, 0.91, and 0.97, respectively, along Jhelum Valley road is 0.83, 0.81, 0.93, and 0.95, respectively, while along Kohala road is 0.89, 0.88, 0.89, and 0.92, respectively. The produced LSMs through ML (i.e., RF and LR) showed better prediction accuracies than WoE and AHP along these three road corridors. The LSMs are categorized into very high, high, moderate, and low susceptible zones along these roads. The LSM generated through hybrid models can facilitate the concerned local agencies to implement landslide mitigation policies for the landslide-prone zones along road corridors.

Landslide susceptibility assessment is essential for development activities and disaster management in the mountainous regions to identify the landslide-prone areas. The present study aimed to evaluate and compare the efficacy of data driven quantitative models of landslide susceptibility assessment using geospatial tools in Jhelum valley of the Himalayas. This area suffers from extreme rainfall events due to the local climate and has experienced significant and widespread landslide events in recent years. Four probabilistic data-driven models are employed for this purpose, which includes the weight of evidence (WOE), information value method (IVM), frequency ratio (FR), and certainty factor (CF). These assessed models are based on integrating landslide contributing factors and a ground truthing-based landslide inventory of 437 landslides. The landslide susceptibility maps were presented by categorizing the study area into very low to very high susceptibility zone by Jenks natural breaks method. The performance of models was evaluated by a sensitivity analysis using Receiver Operator Curve (ROC) method. The ROCvalidated results of success rate curves for WOE, IVM, FR and CF were 80 %, 78 %, 77 %, and 76 % respectively. The prediction rate curve of WOE, IVM, FR, and CF was 78 %, 77 %, 75 %, and 78 % respectively. The results showed the reasonable efficiency of applied models for landslide susceptibility assessment in the study area and applicable to regions with similar geomorphological conditions. Conclusively, the comparison of applied models revealed the promising results of used approaches. [ABSTRACT FROM AUTHOR]

Coal is an organoclastic sedimentary rock, composed essentially of lithified plant debris. Coal is an important energy source and is available in all over the world. Pakistan possesses huge coal reserves across the country that can play a pivotal role in development of the country overcoming the energy crises prevalent in four seasons. Government of Pakistan is seriously looking for alternative resources other than hydro and thermal energy to meet the ongoing and growing energy requirements. Most of the world's lignite deposits are located in Asia. Pakistan is among the top most lignite coal-bearing countries in Asia possessing almost 97% of the coal reserves bearing lignite variety. The rest of the grades are of sub-bituminous to bituminous content. Amongst all the reserves in Asia, 3% are located in Pakistan. The major coal reserves in Punjab are found in Eastern & Central Salt Range and Trans-Indus Range. Most of the region's coal and carbonaceous coal deposits are located in the Paleocene Patala Formation, with a minor occurrence in the Tobra formation of the Permian age. Mining techniques are being pursued viz; room and pillar method and longwall system that are commonly applied in the areas. Supports are provided without geotechnical evaluation. Timbering is provided as supports in these coal mines. The analysis of accidents shows that roof collapse and fall of side walls are the most common and frequent type of fatal accidents making almost of total accidents. [ABSTRACT FROM AUTHOR]