10 research outputs found
Landslide Distribution Analysis and Susceptibility Mapping: A Case Study from Haveli District, Pakistan
No full textIntegrated frequency ratio-analytical hierarchy and geospatial techniques-based earthquake risk assessment in mountainous cities: a case from the Northwestern Himalayas
No full textEarthquake risk management (ERM) needs proper planning and mitigation measures to minimise adverse impacts. In this context, we quantify the earthquake risk based on hazard, exposure, and vulnerability in the Northwestern Himalayas, Pakistan. A complete earthquake catalogue along with topographic, geo-environmental, seismic, and social causative factors were integrated into a geospatial environment for earthquake risk profiling. The integrated frequency ratio-analytical hierarchy process (FR-AHP) techniques were employed to measure the causative factor weights and historical earthquake distribution within the causative factor classes for hazard assessment. Simultaneously, AHP was utilised to calculate the vulnerability. The exposure was obtained by integrating the hazard and the land use map to estimate damage and loss. Finally, the risk was estimated and mapped across the study area at high resolution. The developed AHP model for vulnerability and hazard showed high accuracy for training and validation data sets (i.e. 98% and 93%, respectively). While there is an evident geographic disparity in the estimated risk, the results show that ∼25% of areas fall under the “very high-risk” zones where population and building density are high near active fault zones. The current study offers actionable insights for risk-reduction initiatives in the mapped high-risk zones.link_to_subscribed_fulltex
An integrated approach of support vector machine (SVM) and weight of evidence (WOE) techniques to map groundwater potential and assess water quality
No full textAbstract This study addresses the critical need for effective groundwater (GW) management in Muzaffarabad, Pakistan, amidst challenges posed by rapid urbanization and population growth. By integrating Support Vector Machine (SVM) and Weight of Evidence (WOE) techniques, this study aimed to delineate GW potential zones and assess water quality. This study fills the gap in applying advanced machine learning and geostatistical methods for accurate GW potential mapping. Eight thematic layers based on topography, hydrology, geology, and ecology were utilized to compute the GW potential model. Additionally, water quality analysis was performed on collected samples. The findings indicate that flat and gently sloping terrains, areas with an elevation range of 611 –687 m, and concave slope geometries are associated with higher GW potential. Additionally, proximity to drainage and high-density lineament zones contribute to increased GW potential. The results showed that 31.1% of the area had excellent GW potential according to the WOE model, whereas the SVM model indicated that only 20.3% fell in the excellent potential zone. Results showed that both models performed well in the delineating GW potential zones. Nevertheless, the application of the SVM method is highly recommended which will be benefited in GW resources management related to urban planning. The study also evaluates the spatial distribution of GW quality, with a focus on physical and chemical parameters, including electrical conductivity, pH, turbidity, total dissolved solids, calcium, magnesium, chloride, nitrate, and sulphate. Bacterial contamination assessment reveals that 76% of spring water samples (30 out of 39 samples) are contaminated with E.coli, raising public health concerns. Based on the chemical analysis of GW samples the study identified exceedances of WHO guidelines for calcium in two samples, magnesium in seven samples, sulphate in ten samples, and nitrate levels were below the WHO guideline across all samples. These results highlight localized chemical contamination issues that require targeted remediation efforts to safeguard water quality for public health
An evaluation of the geotechnical characteristics of Gahirat marble using empirical methods: A case study from the Chitral area, Khyber Pakhtunkhwa, North Pakistan
No full textThe use of marble as a building or dimension stone is one of the most growing commodities throughout the world. Due to the rapid increase in urbanization, dimension stones are commonly used in building interiors as well as exteriors. Marble, being a metamorphic rock is very desirable to be utilized in structural as well as decorative purposes. The study presented herein evaluates the geotechnical characteristics of the Gahirat marble in the Chitral area, Pakistan. For this purpose, 40 representative samples of the Gahirat marble from ten different vicinities of the Chitral area were collected and tested to determine the correlation amongst the different geotechnical properties of the studied rock. The uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), water absorption, specific gravity, point load strength (PLS), Schmidt rebound hammer, ultrasonic pulse velocity (UPV), and soundness, tests were performed to evaluate the strength of the Gahirat marble for its possible use as a dimension and building stone. An average of 12 measurements for each of the ten rock sample collection vicinities were performed. The statistical analysis shows that a fairly strong linear positive correlation (R2 = 0.80, 0.81, 0.84, 0.92) exists between UCS along with BTS, specific gravity, water absorption PLS and UPV. The maximum values obtained for the 12 measurements regarding the mechanical properties such as UCS, BTS, PLS and Schmidt hammer rebound number were 97.25 MPa, 11.34 MPa, 6.75 MPa and 51, respectively. The maximum values of the physical properties such as water absorption, soundness, specific gravity and UPV tests were calculated to be 0.08%, 0.38, 2.68 and 5.04 km/s, respectively. The results of the uniaxial compressive strength, Brazilian tensile strength, point load strength and Schmidt hammer rebound tests indicate that the Gahirat marble shows high resistance to crushing and bending effects while the specific gravity values indicate its ability to bear the impact of the degree of polishing and grinding. The water absorption and soundness test values indicated that the Gahirat marble is appropriate to be utilized for floor tiles and outdoor cladding due to its resistance against weathering and low rate of the water absorption
Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling
No full textLandslide predictive performance is expected to vary with different sampling techniques, such as landslide random and cluster sampling. Current advancements in remote sensing technologies and machine learning (ML) have enhanced landslide prediction performance. The Himalayan Mountain range in Pakistan poses an unadorned threat to the ecosystem and valley population because of landslide occurrence. The present study explores, and tests alternative sampling technique based on spatial pattern characterization in the wake of increased landslide prediction efficacy, rather than a renowned random technique for training and testing sampling. Thereupon, landslide inventory data with 17 geo-environmental factors (i.e. topographic, hydrological and seismic factors) were determined. Landslide cluster patterns were confirmed by the Nearest Neighbor Index (NNI) method and after getting the cluster patterns, the predicted performance of landslide sampling was tested using ML and statistical methods. Advanced ML algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Naive Bayes (NB), K-nearest Neighbors (KNN) and statistical methods including Weight-of-Evidence (WofE) and Logistic Regression (LR) were used and validated. The landslide-prone district of Azad Jammu and Kashmir (Neelum Valley), Kashmir Himalayas, Pakistan, was selected as a case study. Prediction performance rates are high with area under the curve (AUC) ranging from 0.802 to 0.912; accuracy (ACC) ranges from 0.78 to 0.89, and kappa ranges from 0.50 to 0.68 with cluster sampling technique, whereas the performance was low with random sampling technique, with AUC ranges from 0.768 to 0.895; ACC ranges from 0.74 to 0.86 and kappa ranges from 0.48 to 0.64. The descending order of accuracy of the six algorithms was XGboost, RF, KNN, NB, LR and WofE. Our results confirmed that the landslides followed cluster patterns in the study area, and ML algorithms with cluster training samples positively affected landslide susceptibility prediction with a statistically significant difference. The outcomes support the hypothesis that using landslides spatial natural existence, as training samples, instead of random concepts, improves the prediction ability; and highlights that alternative landslide partitioning technique could be a practicable and robust choice for landslides prediction modelling
Comparison of landslide susceptibility models and their robustness analysis: a case study from the NW Himalayas, Pakistan
No full textMachine learning methods are considered as most effective approaches to accomplish landslide susceptibility analysis around the globe. Landslide susceptibility maps (LSMs) have been frequently executed by statistical models in NW Himalaya. However, the comparison and applications of the statistical models with modern machine learning techniques has not been fully explored in this region. Hence, this study aims to compare the predicted performance of statistical and popular machine learning models to explore robust landslide prediction model in the landslide-prone area of NW Himalaya and investigate the compensations and limitations of these models to grasp a more precise and consistent result. This study presented machine learning approaches based on the artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR) and the statistical methods based on the frequency ratio (FR), information value (InfoV) and weight of evidence (WoE). For this purpose, first an inventory map of 1507 landslides was prepared and randomly divided into training (70%) and testing (30%) dataset. Furthermore, 12 landslide conditioning factors (LCFs) were extracted from geospatial dataset to prepare thematic layers in ArcGIS. Thereafter, factor analysis was performed to eliminate colinear and least important variables which can mislead the results. The results showed that all selected LCFs are noncolinear and have significant contribution on landslides initiation, however, lithology, slope angle, annual rainfall and landuse were most influential factors. For modeling purpose, landslide inventory was correlated against all LCFs and trained into six models to produce respective LSMs. Finally, the performance of produced LSM models was validated and compared through area under receiver operating characteristic curve (AUROC), Accuracy, Recall, F1-score and Cohen’s Kappa coefficients to assess the robustness of employed models. The results exhibit that the performance scores of machine learning models were considerably superior than statistical models. While, the AUROC values based on validation dataset indicate that LR (0.89) has better prediction ability followed by SVM (0.86), ANN (0.84), FR (0.83), InfoV (0.82) and WoE (0.81) in this study. Therefore, it is reasoned out that the machine learning methods are more reliable in generating adequate LSMs. However, the LR is recommended as most efficient model for predicting landslide susceptible zones in study region and thus can be considered as robust model for landslide susceptibility assessment in similar geo-environmental regimes
Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan
No full textThe Himalayan region, a rugged mountain zone is among the most susceptible zones to the landslide hazard due to its terrain, geography, and active tectonics. Machine learning (ML) techniques are most advanced and precise methods to develop landslide susceptibility model (LSM). The current study was designed to analyze and assess the landslide susceptibility using ML approaches for District Hattian Bala, NW Himalayas, Pakistan. The historical satellite imageries are used to generate spatiotemporal landslide inventories of year 2005, 2007 and 2012. A spatial database was created pertaining to topographic, environmental, geologic, and anthropogenic factors including slope, aspect, elevation, curvature, plane curvature, profile curvature, topographic wetness index (TWI), lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI) and land use/ land cover (LULC). These LCFs were selected to analyze periodic landslide susceptibility in the region. The experimental design utilized 349, 393, and 735 landslide inventory of 2005, 2007, and 2012 respectively. Two ML models, i.e., Random Forest (RF) and Logistic Regression (LR) were applied to assess landslide susceptibility determine by thirteen landslide causative factors (LCFs). The spatiotemporal landslide inventory was partitioned into training (70%) and testing (30%) landslides for respective years to check the prediction accuracies of selected ML models. Comparative analysis of different LSMs was performed by the Receiver Operator Curves – Area Under Curves (ROC-AUC). The resultant accuracy, MAE, RMSE, Kappa, Precision, Recall, F1 indicated that RF outperformed the LR model. The study aims to minimize losses to lives and potential economic damage linked with recurrent slope instabilities in the region. It is anticipated that use of ML algorithms would support concerned authorities and organizations to effectively plan and manage landslide hazard in the region.Validerad;2023;Nivå 2;2023-04-20 (hanlid);Funder: Higher Education Commission (HEC) Pakistan (8899, NRPU)</p
Eocene nannofossils and paleoenvironmental reconstruction of the Kuldana Formation in Yadgar area, Muzaffarabad, northern Pakistan
No full textExacerbating landslide risks under future climate change and land use scenarios: evidence from the Western Himalayas in Pakistan
No full textLandslides are exacerbating in the climate-sensitive Western Himalayas, threatening populations, infrastructure, and ecosystems. This study develops a dynamic risk assessment framework using static stressors and dynamic drivers, including climate projections from CMIP6, land use forecasts via CA-Markov, and Shared Socioeconomic Pathways (SSP1–2.6, SSP2–4.5, SSP5–8.5). Landslide susceptibility was modeled using an integrated Frequency Ratio–Analytic Hierarchy Process approach, validated through ROC analysis and Seed Cell Area Index. Population exposure used gridded SSP datasets, while vulnerability was assessed using the static Global Relative Deprivation Index due to limited future socioeconomic data. Results show extreme precipitation dominates the risk at high elevations, while land-use change intensifies in peri-urban corridors. Landslide risk areas are projected to expand under all SSPs, with a significant increase from 5.34% in 2020 to 9.04% by 2060 under SSP2−4.5, a relative change of 69.3%. Hotspot transition analysis reveals that moderate-risk zones in 2020 will evolve into high-risk areas by 2060, mainly near expanding infrastructure on steep slopes. ~61% population resides in vulnerable zones, highlighting the urgency for targeted adaptation. The framework offers insights into slope zoning, early warning systems, and resilient infrastructure, supporting the UNDRR's ‘Early Warning for All’ initiative and IPCC adaptation goals
Application of statistical and machine learning techniques for landslide susceptibility mapping in the Himalayan road corridors
No full textLandslides 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, andmachine learning (ML), along themain 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 performancematrix 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 Neelumroad 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 landslideprone zones along road corridors.Validerad;2023;Nivå 2;2023-01-19 (joosat);Licens fulltext: CC BY License</p
