Frequency Ratio Values Research Articles

Landslide susceptibility assessment along highways (SH-12 and NH-717A) in Darjeeling Himalayas

Landslides are the utmost damaging natural along with anthropogenic hazards which cause huge damage to assets and human lives in hilly environments all over the world. The key aim of this study is to explore landslide susceptibility (LS) maps along highways (SH-12 and NH-717A) in the Darjeeling Himalayas, India. Four data-driven bivariate statistical models (BSMs) were used to develop LS maps based on geospatial techniques. A total of 90 (100%) polygons with different size of landslide locations were identified to develop landslide inventory/distribution (LI/LD) map by incorporating Google Earth and satellite imageries, and filed investigation through the global positioning system (GPS). The LI/LD map is distributed into testing 30% (27 landslide locations), and training 70% (63 landslide locations) of the models. Eighteen landslide causative factors (LCFs) were selected to develop LS maps. Multicollinearity analysis showed that there is no collinearity problem in the LCFs. The receiver operating characteristics (ROC) curve and frequency ratio (FR) value techniques were applied for validation. The relative importance of each LCF for each model has been measured using Jack-knife test. The LS maps were divided into six landslide susceptibility zones (LSZs). The ROC curve showed the accuracy of the LSZ maps is 93.50% for FR, 85.90% for evidential belief functions (EBFs), 81.80% for fuzzy membership (FM) using FR, and 77.20% for FM using cosine amplitude (CA) approaches respectively. Among all models, the FR model showed the highest percentage of accuracy for LS assessment and prediction. The FR values of six LSZs rise from very low to very high LSZs which indicate a positive correlation between LSZs and FR values. The results of Jack-knife test showed that terrain ruggedness index (TRI) factor has the highest relative importance for all models in LSZ mappings. The study findings will help to the planner, and policymakers for spatial planning, implement slope management strategies, land use management in mountainous environments.

Landslide susceptibility mapping using modified frequancy ratio method in Correb area, South Wollo, North-Western Ethiopia

Correb area in Mekdela Woreda, South-Wollo Zone, North-Western Ethiopia is one of the most landslide affected region. The primary aim of this study was to assess the landslide susceptibility of the area and create a landslide susceptibility map. A bivariate statistical frequency ratio model was used to accomplish this goal. Using fieldwork and Google Earth picture interpretation, a map of landslides was made. A total of 524 landslides were identified and then classified in to training landslide (75%) for model development and validation landslide (25%) for model validation. Eight landslide causative factors slope, aspect, elevation, distance to drainage, distance to spring, slope material, distance to lineament, and vegetation cover were integrated with training landslides in order to determine frequency ratio value(weight) for each class of landslide causative factors. Furthermore, each causative factor’s prediction rate or weight was established. Relative frequency values were allocated to the appropriate factor classes and prediction rate values were allocated to the appropriate causative factors and summed using a raster calculator algorithm in order to produce the landslide susceptibility map. The final landslide susceptibility map revealed that 27.54% (31.52 km2) of the study area falls under very low susceptible, 30.35% (34.74 km2) low susceptible, 21.36% (24.44 km2) moderate susceptible, 14.74% (16.88 km2) high susceptible, and the rest 6.01% (6.88 km2) as very high susceptible. This shows that more than 20% of the area has probability and risk of landslide while about 80% has relatively low probability and risk of this natural hazard. The performance of modified frequency ratio model for landslide susceptibility mapping was validated using simply overlay, relative landslide density index, and receiver operating characteristics curve. Both training and validation landslides were rare in the very low and low susceptibility classes and strongly concentrated in the high and very high susceptibility classes, according to validation using simple overlay and the relative landslide density index. The implemented model performed exceptionally well, as seen by the receiver operating characteristics curve, which showed 82.6% success rate and an 83.1% prediction rate. In general, the model produced reasonable accuracy. The resultant map would be useful for general land use planning, site selection, and landslide prevention and mitigation programs.

The CIPM list ‘Recommended values of standard frequencies’: 2021 update

This paper gives a detailed account of the analysis underpinning the 2021 update to the list of standard reference frequency values recommended by the International Committee for Weights and Measures. This update focused on a subset of atomic transitions that are secondary representations of the second (SRS) or considered as potential SRS. As in previous updates in 2015 and 2017, methods for analysing over-determined data sets were applied to make optimum use of the worldwide body of published clock comparison data. To ensure that these methods were robust, three independent calculations were performed using two different algorithms. The 2021 update differed from previous updates in taking detailed account of correlations among the input data, a step shown to be important in deriving unbiased frequency values and avoiding underestimation of their uncertainties. It also differed in the procedures used to assess input data and to assign uncertainties to the recommended frequency values, with previous practice being adapted to produce a fully consistent output data set consisting of frequency ratio values as well as absolute frequencies. These changes are significant in the context of an anticipated redefinition of the second in terms of an optical transition or transitions, since optical frequency ratio measurements will be critical for verifying the international consistency of optical clocks prior to the redefinition. In the meantime, the reduced uncertainties for optical SRS resulting from this analysis significantly increases the weight that secondary frequency standards based on these transitions can have in the steering of International Atomic Time.

Experimental and numerical investigation on the effect of frequency combinations on PFOA defluorination by dual-frequency ultrasound coupling persulfate

Due to the synergistic contribution, dual-frequency ultrasound can significantly enhance the degradation of organic contaminants, but the effects of different frequency combinations have not been clarified and need to be explored. In this study, several frequency combinations were designed firstly both in experimental exploration and numerical simulation within the dual-frequency ultrasound coupling persulfate (PS) system to investigate the effect of frequency combinations on the degradation of perfluorooctanoic acid (PFOA). The monitoring parameters included the defluorination percentage and the simulated maximum expansion ratio (Rmax/R0). The simulated maximum expansion ratios in different frequency combinations showed a high correlation with their defluorination results, indicating that the discrepancy in defluorination effects of different frequency combinations was mostly attributed to their various maximum expansion ratios. Additionally, various maximum expansion ratios could result in different maximum solution temperatures, and the increase in temperature was found positive for PFOA defluorination in this study. In addition, the synergistic parameters that affected the maximum expansion ratio were analyzed, including the phase difference, frequency ratio, and frequency value. The obtained results showed that the maximum expansion ratio varied with different phase differences, and the frequency ratio could affect the variation trend. For the same fundamental frequency, the larger the frequency ratio, the smaller the maximum expansion ratio. Under the same phase difference and frequency ratio, the larger the frequency value, the lower the cavitation.

Landslide susceptibility prediction modelling based on semi‐supervised XGBoost model

In the process of landslide susceptibility prediction (LSP) modelling, there are some problems in the model dataset relating to landslide and non‐landslide samples, such as landslide sample errors, subjective randomness and low accuracy of non‐landslide sample selection. In order to solve the above problems, a semi‐supervised machine learning model for LSP is innovatively proposed. Firstly, Yanchang County of Shanxi Province, China, is taken as the study area. Secondly, the frequency ratio values of 12 environmental factors (elevation, slope, aspect, etc.) and the randomly selected twice non‐landslides are used to form the initial model datasets. Thirdly, an extreme gradient boosting (XGBoost) model is adopted for training and testing the initial datasets, so as to produce initial landslide susceptibility maps (LSMs) which are divided into very low, low, moderate, high and very high susceptibility levels. Next, the landslide samples in initial LSMs with very low and low susceptibility levels are excluded to improve the accuracy of landslide samples, and the unlabelled twice non‐landslide samples in initial LSMs with low and very low susceptibility levels are randomly selected to ensure the accuracy of non‐landslide samples. These new obtained landslide and non‐landslide samples are reimported into XGBoost model to construct the semi‐supervised XGBoost (SSXGBoost) model. Finally, accuracy, kappa coefficient and statistical indexes of susceptibility indexes are adopted to assess the LSP performance of XGBoost and SSXGBoost models. Results show that SSXGBoost model has remarkably better LSP performance than that of XGBoost model. Conclusively, the proposed SSXGBoost model effectively overcomes the problems that the accuracy of landslide samples needs to be further improved and that non‐landslide samples are difficult to select accurately.

Landslide Susceptibility Mapping Using GIS and Bivariate Statistical Models in Chemoga Watershed, Ethiopia

This study aimed to map the landslide susceptibility in the Chemoga watershed, Ethiopia, using Geographic Information System (GIS) and bivariate statistical models. Based on Google earth imagery and field survey, about 169 landslide locations were identified and classified randomly into training datasets (70%) and test datasets (30%). Eleven landslides conditioning factors, including slope, elevation, aspect, curvature, topographic wetness index, normalized difference vegetation index, road, river, land use, rainfall, and lithology were integrated with training landslides to determine the weights of each factor and factor classes using both frequency ratio (FR) and information value (IV) models. The final landslide susceptibility map was classified into five classes: very low, low, moderate, high, and very high. The results of area under the curve (AUC) accuracy models showed that the success rates of the FR and IV models were 87.00% and 90.10%, while the prediction rates were 88.00% and 92.30%, respectively. This type of study will be very useful to the local government for future planning and decision on landslide mitigation plans.

Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management

The present research is conducted in the southern region of Khyber Pakhtunkhwa, Pakistan, to identify groundwater potential zones (GWPZ). We used three models including Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) with twelve parameters (elevation, slope, aspect, curvature, drainage network, LULC, precipitation, geology, Lineament, NDVI, road, and soil texture, that have been prepared and integrated into ArcGIS 10.8. The reliability of the applied models’ results was validated using Area Under the Receiver Operating Characteristics (AUROC). The GWPZ were reclassified into five classes, i.e. very low, low, medium, high, and very high zone. The area occupied by mentioned classes using WOE are very low (10.14%), low (19.58%), medium (26.75%), high (27.10%), very high (16.40%), while using FR are very low (20.93%), low (32.38%), medium (18.92%), high (13.13%), very high (14.61%) and using IV are very low (14.41%), low (17.17%), medium (29.01%), high (25.85%), and very High (13.53%). The Success Rate Curve of WOE, FR, and IV were 0.86, 0.91, and 0.87, while the Predicted Rate Curve values were 0.89, 0.93, and 0.90, respectively. The results revealed that all applied statistical models performed very well to delineate GWPZ. However, use of the FR technique is strongly encouraged to evaluate the GWPZ, and its findings are especially useful for managing groundwater resources in urban planning. Our approaches for assessing the GWPZ mapping can be applied in any region with similar scenarios and are recommended as a helpful tool for policymakers to manage groundwater.

Frequency ratio analysis to determine the landslide susceptibility in East Sikkim district of Indian Himalayan region

Landslides are a significant natural disaster causing damage to many mountainous regions worldwide including the Indian Himalayan region. In the East Sikkim district of the Eastern Himalayas, the most used bivariate frequency ratio (FR) model was utilized with high-resolution satellite imagery to understand the susceptibility of the region to landslides. Conditioning factors such as slope aspect, slope angle, slope curvature, drainage density, land use and land cover (LULC), normalized difference vegetation index (NDVI), lithology, and geomorphology were considered in the analysis. LULC is the most crucial factor contributing to landslide susceptibility with a normalized FR value of 14.1. Slope and geomorphology followed closely with values of 12.5 and 11.8 respectively. In contrast, the least important factors were slope aspect and lithology with values of 8.7 and 9.3 respectively. These results can be used to prioritize landslide conditioning factors (LCF) and generate a final landslide susceptibility map (LSM). By adding the values of all LCFs, a landslide susceptibility index was obtained, and the LSM was zoned into high, medium, and low susceptibility classes covering 23.4%, 44.4%, and 32.2% of the study area respectively. The validity of the method used was confirmed using a receiver operating characteristic curve which yielded an accuracy of 78%. The findings highlight the importance of LULC, slope, and geomorphology as critical factors in landslide susceptibility in the East Sikkim district of the Eastern Himalayas.

Landslide Occurrences under Diverse Landuse/cover in Lower Kosi Watershed, Central Himalaya

Abstract Present study was related to land degradation after landslide and risk analysis of Lower Kosi watershed in Central Himalaya using RS and GIS. The study was aimed to identifying the relation of land use with landslide activity in Himalayan terrain and landslide management. The landslide inventory map was prepared for the detailed study with the help of Google Earth Image and field verifications. Landslide had been sub-categorized into two categories; natural and road induced. The land use map was prepared from Google Earth image followed by toposheets. Statistical analysis of landslide frequency and density as well as frequency and density ratios were applied. The collected data was evaluated in combination with GIS. The relationship between land use categories and landslide occurrences was determined. The landslide frequency ratio (FR) and density ratio (DR) were analyzed. The FR and DR value of 1 is an average value. Hence, the value of ratio more than 1 signifies a strong relationship between landslides and the given factor while ratio values less than 1 registers a poor relationship. The FR and DR values were >1.0 in the areas of fallow land, barren/rocky land, agricultural land and open forest revealing a strong relation to landslide processes while the lesser values of FR and DR in ‘dense forest’ area determines the impact of sufficient vegetation cover on the least occurrence of landslides. This study may be applicable for new and existing land use planning projects as the adopted methodology provides rapid estimation of landslide vulnerability of the area.

Mapping of Landslide Potential Zone with FRM Method and Mitigation in Ayah, Kebumen Regency

Landslides phenomena occur yearly in Ayah sub-district and various factors persist in the future cause these landslides. The Frequency Ratio Method is one approach to mapping landslide potential. It involves direct data collection of potential landslide points in the field. This method uses parameters such as Lithology, slope, NDVI, Monthly Precipitation, and Land Use to calculate landslide potential. These parameters are overlaid with the frequency of potential landslide points in Ayah sub- district. The resulting frequency ratio value is classified into five potential landslide zones. The findings reveal that high values covering 10.97% and very high values covering 41.75% are distributed across almost all the settlements in Ayah Sub-district, except for the northern area. The high frequency value shows the dominance of steep slope, presence of weathered breccia and limestone lithology as well as agricultural land with fibrous roots and it is also related to the complex changes in land use, such as mining and clearing activities. This highlights the importance of taking preventive and escalation measures among the relevant stakeholders to minimize the impact of this landslide.

Analysis of Flood Susceptibility Using Frequency Ratio Method in Paremang Watershed

The flood in the Paremang Watershed caused damage to the road that connected several districts in South Sulawesi. In addition, it also inundated rice fields and residential areas, causing losses to residents. Therefore, this research was carried out to identify floods, find out the causes of floods that influence the occurrence of floods in the Paremang Watershed, and make a flood hazard map in the Paremang Watershed. This study uses SAR Sentinel-1 imagery to identify floods that occur in the Paremang watershed and frequency ratio analysis to look at the causative factors that influence flooding in the Paremang watershed and project a flood susceptibility map based on the causes of flooding. The results of this study show that in 2017–2021, an area of 3,446.12 ha was affected by flooding from the Paremang Watershed area of 92,717 ha. The land cover factor in the paddy field class with a frequency ratio value of 4.83, the slope factor in the 0-8% class with a frequency ratio value of 4.32, and the altitude factor in the 0-200 masl class with a frequency ratio value of 4.08 are the factors that most frequently occur in that factor. The flood hazard map in the Paremang watershed shows that the downstream part of the Paremang watershed is an area of high and very high susceptibility.

Landslide susceptibility mapping in the Northern part of Los Glaciares National Park, Southern Patagonia, Argentina using remote sensing, GIS and frequency ratio model

The valleys in the northern part of Los Glaciares National Park, Southern Patagonia, Argentina, are highly dynamic due to glacier shrinkage. To improve our understanding of slope instability in deglaciated environments, we generated and analyzed a Landslide Susceptibility Map (LSM). The proposed methodology includes remote sensing, interpretation of geological maps, both performed in a Geographic Information System (GIS), and finally a Frequency Ratio Model (FRM). Factors included in the slope instability analysis were lithology, geomorphology, land cover/land use, slope, aspect, elevation, curvature, distance to geologic fault, and distance to roads and trails. Slope instability events identified in the inventory include moraine landslides, debris flows, and rockfalls. The area is modeled by glacial action and has numerous geographic features related to erosional and depositional processes of glaciofluvial, cryogenic, and paraglacial origin. According to their high frequency ratio values, geomorphology, land cover and land use, lithology, and elevation are the most influential predisposing factors. The three best performing classes are Quaternary deposits, ice-contact topography, and the innermost lateral moraines on the western side of the study area, which surround glaciers and proglacial lakes. The performance and accuracy of the LSM was evaluated and verified using the Area Under Curve (AUC) with an accuracy of 0.804. The study provides knowledge of the deglaciated environment and landslide-prone areas in the study area. In this way it will help to provide tools for action to prevent and manage potential hazards to infrastructure, residents and/or tourists.

GIS-based landslide and rockfall susceptibility zoning in Chepelarska River Basin (Western Rhodope Mountains, Bulgaria)

Landslides and rockfalls are one of the most widespread hazardous phenomena in the Western Rhodopes, Bulgaria. These mass-wasting types occur together with other dangerous phenomena such as river bank erosion and soil degradation, and can cause a lot of infrastructural damage. The main purpose of this paper is to perform GIS-based landslide and rockfall susceptibility zoning and to elaborate susceptibility maps at a scale of а river basin on the example of the Chepelarska River Basin (RB). In order to achieve this goal, nine causal factors have been selected, and map of landslides and field study have been used. These factors were reclassified using histograms and the natural breaks method. In addition, the frequency ratio values of each class have been calculated. To obtain the causal factors impact on sliding and rockfalls, the weights and weighted averages sum of their classes were calculated and analysed. For the complex analysis and elaboration of mass-wasting susceptibility maps, weighted overlay and weighted sum methods have been applied. The obtained results show that from the total 38 landslides and rockfalls in the study area, 26 of them fall into the very high susceptibility zone applying a weighted overlay, and 25 of them fall into the same zone using the weighted sum method. These results and the used methods could be used for other small and medium-sized river basins.

An integrated quantitative and qualitative approach for landslide susceptibility mapping in West Sikkim district, Indian Himalaya

Landslides rank as the third most common natural disaster globally, and the Indian Himalaya Region is no exception, experiencing severe impacts during the rainy season. This study focuses on creating a comparative landslide susceptibility map for the West Sikkim district in India using probabilistic and heuristic approaches. The frequency ratio (FR) and information value (IV) methods are employed for the probabilistic approach, while the analytic hierarchy process (AHP) is used for the heuristic approach. Eleven factors are considered in the analysis. The resulting landslide susceptibility (LS) map demonstrates accuracies of 77% for FR, 74% for IV, and 57% for AHP methods. Preliminary qualitative risk assessment is conducted, incorporating building and population density, as population and buildings are the most vulnerable elements in the society. The LS map with the highest accuracy (from FR) serves as the landslide potential factor, combined with building and population density as the risk damage potential factors for risk zonation. The resulting risk zonation map classifies the study area into high-risk (3%), medium-risk (14%), and low-risk (83%) zones. This study primarily addresses the 3% high-risk area where landslides pose a significant threat to population and infrastructure, aiming to inform policy implementation and mitigation measures.

Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana

In this study, predictive models that characterize gold potential zones within the Josephine Prospecting Licence (PL) Area of Northwestern Ghana have been created by data-driven methods comprising frequency ratio and information value. These predictive models were evaluated using known locations of gold (Au) occurrence datasets and compared to each other. The mineral prospectivity models (MPMs) of gold occurrence areas within the Josephine PL Area were constructed by determining the spatial correlation between known locations of Au occurrences and eight mineralization related factors. The locations of these known Au occurrences, which characterize regions of anomalously high Au geochemical concentration and regions of previous or ongoing artisanal mining operations were identified by using geographic positioning systems (GPS). Eight mineralization related factors (geoscientific thematic layers) over the entire study area composed of analytic signal, lineament density, uranium-thorium ratio, uranium, potassium-thorium ratio, potassium, reduction-to-equator and geology were used to generate the MPMs. The predictive capacity of each of the MPMs generated was determined by employing the area under the receiver operating characteristics curve (AUC). The AUC score obtained for the predictive models produced based on the information value and the frequency ratio approaches were respectively 0.794 and 0.815. The AUC scores generated indicate that the MPMs produced are good predictive models (with an AUC greater than 0.7) and can therefore assist in narrowing down the highly prospective zones of mineral occurrences within the study area. However, the overall predictive potential of the frequency ratio approach was better than the model produced by the information value approach.

Serum neuronal pentraxin 2 is related to cognitive dysfunction and electroencephalogram slow wave/fast wave frequency ratio in epilepsy.

Cognitive dysfunction in epileptic patients is a high-incidence complication. Its mechanism is related to nervous system damage during seizures, but there is no effective diagnostic biomarker. Neuronal pentraxin 2 (NPTX2) is thought to play a vital role in neurotransmission and the maintenance of synaptic plasticity. This study explored how serum NPTX2 and electroencephalogram (EEG) slow wave/fast wave frequency ratio relate to cognitive dysfunction in patients with epilepsy. To determine if serum NPTX2 could serve as a potential biomarker for diagnosing cognitive impairment in epilepsy patients. The participants of this study, conducted from January 2020 to December 2021, comprised 74 epilepsy patients with normal cognitive function (normal group), 37 epilepsy patients with cognitive dysfunction [epilepsy patients with cognitive dysfunction (ECD) group] and 30 healthy people (control group). The mini-mental state examination (MMSE) scale was used to evaluate cognitive function. We determined serum NPTX2 levels using an enzyme-linked immunosorbent kit and calculated the signal value of EEG regions according to the EEG recording. Pearson correlation coefficient was used to analyze the correlation between serum NPTX2 and the MMSE score. The serum NPTX2 level in the control group, normal group and ECD group were 240.00 ± 35.06 pg/mL, 235.80 ± 38.01 pg/mL and 193.80 ± 42.72 pg/mL, respectively. The MMSE score was lowest in the ECD group among the three, while no significant difference was observed between the control and normal groups. In epilepsy patients with cognitive dysfunction, NPTX2 level had a positive correlation with the MMSE score (r = 0.367, P = 0.0253) and a negative correlation with epilepsy duration (r = -0.443, P = 0.0061) and the EEG slow wave/fast wave frequency ratio value in the temporal region (r = -0.339, P = 0.039). Serum NPTX2 was found to be related to cognitive dysfunction and the EEG slow wave/fast wave frequency ratio in patients with epilepsy. It is thus a potential biomarker for the diagnosis of cognitive impairment in patients with epilepsy.

Potential Factors of Landslide Recurrence in Uttaradit, Thailand: A Case Study in Laplae, Mueang Uttaradit, and Tha Pla Districts

Laplae, Mueang Uttaradit, and Tha Pla district, Uttaradit province, Thailand are considered as high potential landslide areas. Still, this disaster is difficult to address because of complex factors controlling its occurrence. Therefore, the prediction of the potential landslide area using a landslide susceptibility map has been able to accomplish as a great strategy for the disaster. A landslide susceptibility map was produced by the geographic information system (GIS) data. The methods were initially conducted by the selection of potential factors related to landslides, which were lithology, slope, aspect, plan curvature, profile curvature, distance to stream, land use, and rainfall. All factors were assigned coefficients weight, and analyses frequency ratio (FR). Then, the weighted variables have been combined and ranked into five different susceptibility levels, which were very low, low, medium, high, and very high. Finally, the produced landslide susceptibility map has been validated by the success rate and prediction rate. After the analysis, the high and the very high landslide susceptibility area were dominantly covered in the northern and northwest parts of the study area; and the factor of slope, land use, and lithology potentially caused the landslide risk indicated by high frequency ratio values. In addition, the produced landslide susceptibility map had high accuracy, about 90% of success rate and prediction rate, calculated from the area under the curve (AUC), this map would be beneficial for geological hazard management and land use planning. The landslide susceptibility map and the GIS-based methods can be applied to the regional area with additional benefits to well-being, society, and the environment.

Hydrometeorological Landslides on the Windward Side of Western Ghats – A Case Study of Kootickal, Kerala, India

On the windward side of Western Ghats, the frequency of landslides has significantly increased in recent years. Kerala had catastrophic landslides and floods in 2018, 2019, 2020, and 2021, resulting in loss of lives and property. On October 2021, a cloudburst occurred in middle Kerala, causing multiple devastating landslides in districts of Kottayam and Idukki. The study focused on how the topographic, physical, geological, and anthropogenic factors in the windward slope of the Western Ghats influence the occurrence of landslides. The landslide susceptibility was analyzed using the frequency ratio method on the basis of recently occurred landslide locations in the Manimala river basin, with special reference to Kootikal. The impact of each factor was analyzed against the GPS coordinates of landslide locations to estimate the frequency ratio value. The findings revealed that the torrential rainfall experienced in Kerala and the anthropogenic activities, especially the watershed management work performed in the area, significantly increased landslide susceptibility of the basin. About 5% of Manimala river basin is highly susceptible to landslides, and this area comes under the Kootikal sub-basin. The study recommends a rigorous geophysical assessment on the influence of watershed management operations on a landslide and an EIA of roads in the Kootickal region.

Rule-based fuzzy inference system for landslide susceptibility mapping along national highway 7 in Garhwal Himalayas, India

The Mountainous terrain,in the Himalayas is experiencing rapid development in a bewildering manner, which makes it more susceptible to landslides. Management and mitigation of landslide hazard begin with its mapping by integrating numerous methods and Geographic Information System (GIS) tools. However, it is difficult to produce reliable landslide susceptibility maps (LSM) with traditional remote sensing and GIS methods due to complexity of the mountainous terrain environments and huge datasets. Therefore, the present study investigates the applicability of Mamdani’s fuzzy inference system (FIS) to produce LSM in Himalayan terrain in India. It is compared with commonly used frequency ratio (FR) and information value method (IVM) approaches. Several causative factors were extracted and used to prepare thematic layers, including slope, aspect, curvature, solar radiance, SPI, TWI, rainfall, soil depth and NDVI. Landslide inventory was also created using google earth images and previously published work. The accuracy estimates for FR, IVM and FIS were performed based on ROC curves. FIS was found to provide an accuracy of 77.7%, followed by IVM (72%) and FR (71%) for LSM. The current study is a prototype for further studies in the Garhwal Himalayas and similar terrains, based on the vigorous Mamdani’s techniques of fuzzy inference theory. The outcomes of this work propose that an expert’s knowledge-based FIS method can produce an accurate LSM in such a complex terrain. Planners and concerned authorities can use the results further for landslide management and mitigation.

Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping

Groundwater is a crucial natural resource that varies in quality and quantity across Khyber Pakhtunkhwa (KPK), Pakistan. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Kohat region, Pakistan by integrating twelve thematic layers. In the current research, Groundwater Potential Zone (GWPZ) were created by implementing Weight of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) models of the Kohat region. In this study, we used Sentinel-2 satellite data were utilized to generate an inventory map of groundwater using machine learning algorithms in Google Earth Engine (GEE). Furthermore, the validation was done with a field survey and ground data. The inventory data was divided into training (80%) and testing (20%) datasets. The WOE, FR, and IV models are applied to assess the relationship between inventory data and groundwater factors to generate the GWPZ of the Kohat region. Finally, the current research results of Area Under Curve (AUC) technique for WOE, FR, and IV models were 88%, 91%, and 89%. The final GWPZ can aid in better future planning for groundwater exploration, management, and supply of water in the Kohat region.