Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya.

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling–Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling–Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling-Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling-Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.ArticleNATURAL HAZARDS. 65(1):135-165 (2013)journal articl

Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning

Landslide susceptibility assessment in the Rangun Khola watershed of far western Nepal

Considering the serious threat of landslides to life, property, and the environment, this study aimed at exploring past landslides (2005-2020) to evaluate landslide susceptibility. The study is carried out in the Rangun Khola watershed, in western Nepal covering an area of 488 km2. The landslide inventory map was prepared, recognizing 494 landslides, among them 70% were used for susceptibility mapping, and the rest 30% for validation purposes. The size of the landslide was found in the range of 103.53 m2 to 149120.1 m2, with an average of 4677.35 m2. Frequency ratio (FR) and logistic regression (LR) models were implemented for landslide susceptibility assessment based on the various intrinsic factors. The validity of the models was assessed by using receiver operating characteristic (ROC) curves. The success rate was 87.6% for the LR model with a prediction rate of 87.3% indicating a good degree of fit. Similarly, with a success rate of 76.4% and a prediction rate of 75.1%, the result obtained from the FR model was a fairly good performance. Thus, both exhibited reasonably good accuracy in predicting the susceptibility of the landslide and are considered to be in land management and hazard mitigation, and policy formulating purposes.</jats:p

Distribution and Dynamic Behaviors of Landslide in Rangun Khola Watershed of the Western Nepal

Being a major devastating hazard, the study of landslides in Nepal Himalaya is very essential. For controlling and mitigate measures, understanding the behaviors and distribution of landslides over the temporal and spatial range is indispensable. The current study is carried out in the Rangun Khola watershed of western Nepal which spreads from Mahabharat Range (2,500m) to Dun valley covering an area of 489.39 km2. Polygon-based landslide inventory within the temporal range of 18 years (2003 to 2020 AD) was prepared by using temporal series of Google Earth Pro, Sentinel-2 images, and Landsat images, which were verified during the field visit. The number of landslides and area covered in different spatial units and temporal intervals were analyzed using the Q-GIS. In total, 494 landslides were identified and the area covered by the landslide was 0.47% of the total study area. Landslides in this area are highly dynamic with different activity states and temporal fluctuation. The number of landslides were highest, i.e., 143, in 2005 and the Upper Siwalik region consist of a large number of landslides making them highly prone to landslide events. The presence of thrust and faults was also found to be influencing the landslides and size distribution. The study will be useful for further researches to map susceptibility and hazard and also for policymakers to understand landslide status to reduce the risk.</jats:p

Evaluation of Post-Bronchodilator Reversibility in Patient with Chronic Obstructive Pulmonary Disease and Asthma

Obstructive lung disease is a group of disorders comprising Chronic Obstructive Pulmonary Disease (COPD) and asthma. It is one of the most common causes of morbidity and mortality worldwide. COPD is a preventable and treatable disease characterized by persistent respiratory symptoms and airflow limitation, whereas asthma is reversible episodes of recurrent wheezing, cough, breathlessness, and chest tightness. It is sometimes difficult to distinguish COPD from asthma when COPD patients present with significant post-bronchodilator reversibility. Spirometry is the gold standard test to diagnose obstructive airway disease. We carried out a hospital-based cross-sectional study in Nepal Medical College Teaching Hospital from January 2018 to December 2018. One hundred and ninety eight patients who met the inclusion criteria underwent spirometry. Basal and post-bronchodilator FEV1, FVC, FEV1/FVC, and reversibility of FEV1 were measured. The majority of the patients enrolled in the study were males (n=100). The mean age of the patients was 49.3±17.0 years. Most of the patients were above 60 years of age (n=68). Clinical diagnosis of asthma was made in 113 (57%) and COPD in 85 (43%) patients. Post-bronchodilator reversibility was observed in 48 (42%) asthmatic and 19 (22%) COPD patients. Post-bronchodilator reversibility was statistically significant in asthmatic patients (p=0.032). Post-bronchodilator reversibility was observed in COPD patients as well. Therefore, post-bronchodilator reversibility alone may have a limited role in differentiating COPD from bronchial asthma. However, spirometry is mandatory to diagnose a patient with obstructive lung disease.</jats:p

Comparison of Heffner Criteria and Light Criteria in Differentiating Exudative and Transudative Pleural Effusion

Pleural effusion is present when there is &gt;15ml of fluid is accumulated in the pleural space. It can be divided into two types; exudative and transudative pleural effusion. Tuberculosis and parapneumonic effusion are the common cause of exudative pleural effusion whereas heart failure accounts for most of the cases of transudative pleural effusion. This study was a hospital based cross sectional study performed at Nepal Medical College during the period of January 2016-December 2016. A total of 50 patients who fulfilled the inclusion criteria were enrolled. Pleural effusion was confirmed by clinical examination and radiology. After confirmation of pleural effusion, pleural fluid was aspirated and was analysed for protein, LDH, cholesterol. The Heffner criteria was compared with Light criteria to classify exudative or transudative pleural effusion. Among 50 patients, 30 were male and 20 were female. The mean age of patient was 45.4±21.85 years. The sensitivity and specificity of using Light criteria to detect the two type of pleural effusion was 100% and 90.9%, whereas using Heffner criteria was 94.87%, 100% respectively(P&lt;0.01). There are variety of causes for development of pleural effusion and no one criteria is definite to differentiate between exudative or transudative effusion. In this study Light criteria was more sensitive whereas Heffner criteria was more specific to classify exudative pleural effusion. Hence a combination of criteria might be useful in case where there is difficulty to identify the cause of pleural effusion.</jats:p

Characterization of large-scale landslides and their susceptibility evaluation in central Nepal Himalaya

Large-scale landslides (LSL) are characterized by complex nature of failure mechanism, which depend on geological setting and associated factors of the area. The aim of this research is to identify the distribution pattern of LSL and all landslides in the central Nepal Himalaya and evaluation of their controlling factors. 7239 landslides were extracted from the study area by the interpretation of satellite imageries and field surveyed information. 28 landslides were classified as LSL and descriptive statistics were calculated. A comparative susceptibility assessment between all landslides and LSL was performed by frequency ratio model (FRM). Landslide susceptibility assessed from FRM was classified into five categories using the natural breaks method and adjustment from field evidences: very low, low, medium, high, and very high. The very high, high and medium susceptibility classes comprised of 38.91%, 33.29%, 18.76% for all landslides, and 39.51%, 29.65%, 20.98% for LSL. The result clearly indicated that the role of controlling factors varies differently depending upon the size of distributed landslides. To understand the significance of controlling factors for LSL, different potential cases were validated by success rates with area under the curve (AUC). The computed AUC in success rates for LSL is 65% and for overall landslides with similar controlling factors is 75%. The AUC values in different potential cases showed that the prime factors to control the LSL are geomorphology, rainfall, and geological structures.</jats:p

An Accidental Emamectin Benzoate Poisoning In Child: A Case Report

Abstract Background: Emamectin Benzoate has high GABA (Gamma Amino Butyric Acid) receptor affinity and increase chloride membrane permeability. It is the 4'-deoxy-4'-epi-methyl-amino benzoate salt of avermectin B1 (abamectin), obtained from natural fermentation products of Streptomyces Avermitilis.Report: This case report describes the accidental poisoning of Emamectin Benzoate 5% w/ws in a female child. The child was brought to the emergency department(ED) with complaints of nausea, vomiting and abdominal pain. She consumed a packet of “LURA” (5% w/w Soluble Granule (SG) Emamectin Benzoate) supposing it as a packet of “JALJEERA” (a commonly used beverage) since there was no proper labelling. The patient was hemodynamically stable and underwent vigorous gastric lavage with normal saline, activated charcoal and coconut oil. Her blood report was normal for serum electrolytes and renal function. She was admitted in medical ward for symptomatic management and observation. She was given antiemetics, IV fluids and antacids and discharged in 2 days. In follow up after 1 week, she did not have any complain, her gastrointestinal symptoms had completely resolved and she was doing very well.Conclusion: In absence of specific antidote, vigorous gastric lavage with both activated charcoal and coconut oil improves the outcome in Emamectin Benzoate poisoning.</jats:p

Landslide susceptibility analysis using frequency ratio and weight of evidence approaches along the Lakhandehi Khola watershed in the Sarlahi District, southern Nepal

Landslide susceptibility maps are considered as one of the most important keys to limiting and dodging potential landslide consequences worldwide. In the present study, landslide susceptibility maps are prepared using bivariate models: frequency ratio and weight of evidence approaches. At first, randomly selected 80% of landslides i.e.,one hundred eighty landslides are used as training data for the preparation of the model, and the rest 20% of landslides i.e.,forty-five landslides for its validation. Similarly,thematic layers of nine causative factors of landslides such as slope, aspect, curvature, stream density, TWI(Topographic Wetness Index), land use, geology, distance from river and distance from the road have been analyzed for the modeling in ArcGIS. Finally, prepared landslide susceptibility maps are classified into five classes from Very Low to Very High from both methods. The area of Low, Moderate, High, and Very High susceptible classes is also nearly equal. The success rate curve of FR (Frequency Ratio), and WOE (Weight of the Evidence), show accuracy of 71.09%, and 75.62% respectively. Likewise, the prediction rate curve shows 72.87% and 76.66% accuracy on FR and WOE methods respectively. Since the susceptibility maps prepared through both approaches show an accuracy of &gt;70%, the result is deliberated as fair. These maps are useful to all the stakeholders for land use planning and developing mitigation strategies against the consequences of increasing landslides in the Siwalik Hills of Nepal.</jats:p