The Hindukush and Himalaya regions of Pakistan are chronically prone to several geological hazards such as landslides. Studying landslides in these regions is crucial for risk assessment and disaster management, as well as for determining the effects of adverse climatic conditions, infrastructure management, and increasing anthropogenic activities. High-relief mountains in these regions face severe challenges because of frequently occurring landslides and other natural hazards, especially during intensive rainfall seasons and seismic activity, which destroy infrastructure and cause injuries and deaths. Landslides in the Alpuri Valley (Hindukush) and the Neelum Valley (Himalaya) have been activated through high magnitude earthquakes, intensive rainfalls, snowfall, floods, and man-made activities. Landslide susceptibility mapping in these areas is essential for sustainable development as it enables proactive risk management, up-to-date decision-making, and effective responses to landslide hazards, ultimately safeguarding human lives, property, and the environment. In this study, the relative effect method was applied for landslide susceptibility modeling in both study areas to determine the capability to reduce the effects of landslides, and to improve the prediction accuracy of the method. The relative effect is a statistical model that has only been used for very limited time for landslide susceptibility with effective results. A total of 368 (Neelum Valley) and 89 (Alpuri Valley) landslide locations were identified, which were utilized to prepare the reliable landslide inventory using GIS. In order to evaluate the areas at risk for future landslides activities and determine their spatial relationship with landslide occurrences, the landslide inventory was developed with 17 landslide causative factors. These factors include slope gradient, slope aspect, geology, plan curvature, general curvature, profile curvature, elevation, stream power index, drainage density, terrain roughness index, distance from the roads, distance from the streams, distance from fault lines, normalized difference wetness index, land-use/land-cover, rainfall, and normalized difference vegetation index. Finally, the performance of the relative effect method was validated using the success and prediction curve rate. The AUC-validated result of the success rate curve in the Alpuri Valley is 74.75%, and 82.15% in the Neelum Valley, whereas, the AUC-validated result of the prediction rate curve of the model is 87.87% in the Alpuri Valley and 82.73% in the Neelum Valley. These results indicate the reliability of the model to produce a landslide susceptibility map, and apply it to other landslide areas. The model demonstrated a more effective result in the Alpuri Valley, having a smaller area. However, the results are also desirable and favorable in Neelum Valley, with it being a large area. It will assist in general landslide hazard management and mitigation, and further research studies related to future landslide susceptibility assessments in other parts of the region.

Planning and management are necessary tools throughout the world, especially in a mountainous regions where various natural hazards affect the area socially and economically. Landslides are one of the most common natural hazards in mountainous regions throughout the world. For this purpose, qualitative and quantitative methods and landslide susceptibility mapping were used to reduce the probability of landslide occurrence in an affected area and landslide mitigation. The study was focused on landslide susceptibility mapping in Neelum valley using a relative effect model integrated with 17 causative factors of landslides. These factors such as elevation, slope gradient, slope aspect, general curvature, geology, plan curvature, profile curvature, drainage density, stream power index, distance from stream, distance from road, topographic roughness index, Normalized difference wetness index, distance from fault, rainfall, landuse landcover, and Normalized difference vegetative index were used for analysis. Neelum valley is the part of the Himalayan region that is often experiencing landslide hazards frequently. Among other methods, it is a statistical method prepared within the Geographical information system environment to develop landslide hazard zones in Neelum valley. The landslide inventory map was shown the presence of past landslides in the study area by using Google Earth and remote sensing imageries. Total landslides were 368 in number, 30% (110) for testing purposes and 70% (258) for training purposes. The validation of Relative effect model was calculated with the Area under the curve such as the success rate curve and the prediction rate curve. This was adopted to check the validation and optimum landslide susceptibility zone categorization. The success rate curve of the model was 82.15% calculated whereas 82.73% was the predictive rate curve. According to the study, landslide susceptibility mapping was classified into four classes with 18.14% of the area being very high zone, 34.04% of the area being high zone, 30.26% area being moderately susceptible zone and 17.56% of the area being low susceptible to landslide occurrence zone. Hence, the results of this study highlight the spatial information of the area that may face landslide hazards and may be very helpful to planners, engineers, government agencies, stakeholders and other participants for the prevention, mitigation, management, and monitoring of landslide hazards and this model may also be applicable in other landslide areas.

This study explores the evolution of sentiment expressed in a fictional dataset by characters and inspect the correlation between specific events and important change in sentiment. We employ completely sentiment analysis framework to examine carefully selected set of text in order to capture the emotional journeys that the characters take throughout the story. Using VADER and TextBlob, among other techniques, we measure changes in sentiment at different story points and pinpoint the critical areas that elicit strong emotion reaction. Our results shows that significant character interactions, including disputes and reconciliations, and large plot twists, like unexpected disclosures, are highly correlated with the significant changes in sentiment. Tjis study sheds the light on the complex interplay between character emotions and story structure, offering insightful observations about the dynamics.