Relative Operating Characteristic Curve Research Articles

Evaluation of Probabilistic Forecasts of Extreme Cold Events in S2S Models

The probabilistic prediction skill of the weekly forecasts of extreme cold events (ECE) is illustrated and measured in the form of the Brier Skill Score (BSS) and the area under Relative Operating Characteristics (ROC) curves based on the subseasonal-to-seasonal (S2S) prediction project database. The ROC scores show that six S2S models have the good potential predictability skill required for use in ECE probabilistic forecasts, and they were more useful than climatologic probabilistic models in creating forecasts of about 3–4 weeks in length. However, the BSS results show that the actual prediction skill of six models used in ECE probabilistic forecasts are different. The ECMWF model has a good performance, and its actual probabilistic prediction skill of ECE for forecasts of about 3–4 weeks in length was higher than those of climatology, which operates close to its potential predictability. The actual probabilistic prediction skill of the NCEP model for ECE was only about 2 weeks over the extra-tropics, and no skill was recorded over the tropics given its bad reliability, especially over the tropics. BoM, JMA, and CNRM models only have a 1-week actual prediction skill over the Northern Hemisphere extra-tropics, and they have no skill over the rest of the world’s land area. The CNR-ISAC model has a 1-week actual prediction skill over the extra-tropics and about 4 weeks over the tropics. There is still much room for improvement in the prediction ability of models used for ECE. MJO in tropical regions has an important influence on the probabilistic prediction skill of ECE required at middle and high latitudes. When there is an MJO in the initial conditions, the potential predictability and actual prediction skill of ECE probabilistic forecasts over North America in the 3rd week and over Europe in the 3rd–4th weeks are higher than those without MJO.

A practical nomogram and risk stratification system predicting the cancer-specific survival for patients aged >50 with advanced melanoma.

To investigate risk factors for advanced melanoma over 50 years of age and to develop and validate a new line chart and classification system. The SEER database was screened for patients diagnosed with advanced melanoma from 2010 to 2019 and Cox regression analysis was applied to select variables affecting patient prognosis. The area under curve (AUC), relative operating characteristic curve (ROC), Consistency index (C-index), decision curve analysis (DCA), and survival calibration curves were used to verify the accuracy and utility of the model and to compare it with traditional AJCC tumor staging. The Kaplan-Meier curve was applied to compare the risk stratification between the model and traditional AJCC tumor staging. A total of 5166 patients were included in the study. Surgery, age, gender, tumor thickness, ulceration, the number of primary melanomas, M stage and N stage were the independent prognostic factors of CSS in patients with advanced melanoma (P<0.05). The predictive nomogram model was constructed and validated. The C-index values obtained from the training and validation cohorts were 0.732 (95%CI: 0.717-0.742) and 0.741 (95%CI: 0.732-0.751). Based on the observation and analysis results of the ROC curve, survival calibration curve, NRI, and IDI, the constructed prognosis model can accurately predict the prognosis of advanced melanoma and performs well in internal verification. The DCA curve verifies the practicability of the model. Compared with the traditional AJCC staging, the risk stratification in the model has a better identification ability for patients in different risk groups. The nomogram of advanced melanoma and the new classification system were successfully established and verified, which can provide a practical tool for individualized clinical management of patients.

Seasonal forecasting of snow resources at Alpine sites

Abstract. Climate warming in mountain regions is resulting in glacier shrinking, seasonal snow cover reduction, and changes in the amount and seasonality of meltwater runoff, with consequences on water availability. Droughts are expected to become more severe in the future with economical and environmental losses both locally and downstream. Effective adaptation strategies involve multiple timescales, and seasonal forecasts can help in the optimization of the available snow and water resources with a lead time of several months. We developed a prototype to generate seasonal forecasts of snow depth and snow water equivalent with a starting date of 1 November and a lead time of 7 months, so up to 31 May of the following year. The prototype has been co-designed with end users in the field of water management, hydropower production and mountain ski tourism, meeting their needs in terms of indicators, time resolution of the forecasts and visualization of the forecast outputs. In this paper we present the modelling chain, based on the seasonal forecasts of the ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S) Climate Data Store. Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and relative humidity are bias-corrected and downscaled to three sites in the Western Italian Alps and finally used as input for the physically based multi-layer snow model SNOWPACK. Precipitation is bias-corrected with a quantile mapping method using ERA5 reanalysis as a reference and then downscaled with the RainFARM stochastic procedure in order to allow an estimate of uncertainties due to the downscaling method. The impacts of precipitation bias adjustment and downscaling on the forecast skill have been investigated. The skill of the prototype in predicting the deviation of monthly snow depth with respect to the normal conditions from November to May in each season of the hindcast period 1995–2015 is demonstrated using both deterministic and probabilistic metrics. Forecast skills are determined with respect to a simple forecasting method based on the climatology, and station measurements are used as reference data. The prototype shows good skills at predicting the tercile category, i.e. snow depth below and above normal, in the winter (lead times: 2–3–4 months) and spring (lead times: 5–6–7 months) ahead: snow depth is predicted with higher accuracy (Brier skill score) and higher discrimination (area under the relative operating characteristics (ROC) curve skill score) with respect to a simple forecasting method based on the climatology. Ensemble mean monthly snow depth forecasts are significantly correlated with observations not only at short lead times of 1 and 2 months (November and December) but also at lead times of 5 and 6 months (March and April) when employing the ECMWFS5 forcing. Moreover the prototype shows skill at predicting extremely dry seasons, i.e. seasons with snow depth below the 10th percentile, while skills at predicting snow depth above the 90th percentile are model-, station- and score-dependent. The bias correction of precipitation forecasts is essential in the case of large biases in the global seasonal forecast system (MFS6) to reconstruct a realistic snow depth climatology; however, no remarkable differences are found among the skill scores when the precipitation input is bias-corrected, downscaled, or bias-corrected and downscaled, compared to the case in which raw data are employed, suggesting that skill scores are weakly sensitive to the treatment of the precipitation input.

On the verification of ensemble precipitation forecasts over the Godavari River basin

The ensemble forecasts from Numerical Weather Prediction (NWP) models are considered as essential elements for operational hydrologic forecasting. However, these forecasts are burdened with significant inherent biases and cannot be used directly for hydrologic forecasting. Therefore, the present study examines the capability of ensemble precipitation forecasts obtained from two NWP models i.e., a 20-member ensemble from the National Center for Environmental Prediction (NCEP) and a 50-member ensemble from the European center for Medium-term Weather Forecasts (ECMWF). The skill of 70-member multi-model grand ensemble (MME) generated from the NCEP and ECMWF forecasts is also verified in this study. Furthermore, post-processing of the raw ensemble members was carried out using two methods (Quantile Mapping (QM) and Quantile Regression Forests (QRF)) in order to minimize the inherent biases. The analysis was performed over subbasins of an Indian river basin namely the Godavari River Basin. Here, a set of deterministic and probabilistic verification measures (forecast error box plots, correlation coefficient, Relative Mean Error, mean Continuous Ranked Probability Score, spread-skill relationship, rank histograms, reliability diagram, Area under Relative Operating Characteristic Curve) were employed to assess various quality attributes of the forecasts. The findings from the study suggests that QRF post-processed NCEP and MME forecasts are comparatively better than other forecasts in terms of all employed verification measures over all subbasins. The QRF post-processed forecasts were found to be superior than QM post-processed and raw forecasts over all subbasins. The adopted post-processing methods resulted in improved forecasts at shorter lead times. However, the skill of both raw and post-processed forecasts declines at higher lead times over all subbasins.

A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran

Mapping fire risk accurately is essential for the planning and protection of forests. This study aims to map fire risk (probability of ignition) in Marivan County of Kurdistan province, Iran, using the data mining approaches of the evidential belief function (EBF) and weight of evidence (WOE) models, with an emphasis placed on climatic variables. Firstly, 284 fire incidents in the region were randomly divided into two groups, including the training group (70%, 199 points) and the validation group (30%, 85 points). Given the previous studies and conditions of the region, the variables of slope percentage, slope direction, altitude, distance from rivers, distance from roads, distance from settlements, land use, slope curvature, rainfall, and maximum annual temperature were considered for zoning fire risk. Then, forest fire risk maps were prepared using the EBF and WOE models. The performance of each model was examined using the Relative Operating Characteristic (ROC) curve. The results showed that WOE and EBF are effective tools for mapping forest fire risks in the study area. However, the WOE model shows a slightly higher Area Under the Curve value (0.896) compared to that of the EBF model (0.886), indicating a slightly better performance. The results of this study can provide valuable information for preventing forest fires in the study area.

Diagnostic values of history taking, physical examination and KT-1000 arthrometer for suspect anterior cruciate ligament injuries in children and adolescents: a prospective diagnostic study

BackgroundDiagnosing anterior cruciate ligament (ACL) injuries in children and adolescents are more challenging compared to adults. Delayed diagnosis may result in meniscal or chondral injuries. The aim of this study was to determine the diagnostic values of history taking, physical examination and KT-1000 arthrometer for suspect ACL injuries in children and adolescents.MethodsIn this prospective diagnostic study, all children and adolescents (< 18 years) with post-traumatic knee complaints presenting at the out-patient department of the Máxima MC were eligible for inclusion. One experienced knee specialised orthopaedic surgeon was blinded and performed history taking, physical examination and KT-1000 arthrometer measurement. All patients had a magnetic resonance imaging (MRI) for the final diagnosis. Diagnostic values of interest were sensitivity, specificity, positive and negative predictive values (PPV and NPV). The outcomes of the KT-1000 arthrometer were drafted in a relative operating characteristics (ROC) curve to determine the optimal cut-off points.ResultsSixty-six patients were included, of which 50 had an ACL rupture and 16 had no ACL rupture on MRI. Report of a popping sensation during trauma had a specificity and PPV of 100% for diagnosing ACL injuries. The PPV and NPV of the Lachman test (in case of describing end-feel) were 95 and 82%, of the anterior drawer test 87 and 90% and of the pivot shift test 95 and 81% respectively. The optimal cut-off point of the KT-1000 arthrometer at 133 N force was an absolute translation of ≥7 mm with a PPV and NPV of 97 and 88% respectively.ConclusionsReport of a popping sensation during trauma has a specificity and PPV of 100% for diagnosing ACL injuries in children and adolescents. Although potentially difficult in children, the Lachman test, anterior drawer test and pivot shift test have a high PPV and NPV when performed by an experienced orthopaedic surgeon. An absolute anterior translation of ≥7 mm of the injured knee in the KT-1000 arthrometer at 133 N has the highest diagnostic values of all tests for diagnosing ACL injuries.Level of evidence3

Identification of precipitation trend and landslide susceptibility analysis in Miandoab County using MATLAB.

Iranian plateau is a seismically active region. Within this region, northwestern Iran is of high importance. Before proper planning for mitigating the earthquake-induced hazards can be achieved, it is necessary to identify high-risk areas in terms of susceptibility to earthquakes. In this study, landslide susceptibility in Miandoab Country was modeled using the so-called random forest algorithm (RFA) in MATLAB based on records acquired at 67 earthquake hotspots considering 9 factors affecting the earthquake occurrence (i.e., height, slope, direction, distance from fault, distance from river, distance from road, land use, geology, and precipitation). Predictive power of the model and validity of its results were evaluated using relative operating characteristic (ROC) curve and area under the curve (AUC). The assessment results showed very good accuracy of the model (0.97). It was further found that digital height layer, geology, and distance from fault impose the largest contributions into earthquake potential. The results also showed that 53%, 8.3%, and 38.4% of the study area were classified as being at low risk, moderate risk, and high risk of earthquake. Among other climatic elements, the precipitation exhibits the largest fluctuations; we proceeded to evaluate precipitation trends in the study area for a statistical period of 30years. This was practiced by implementing Mann-Kendall nonparametric test in MATLAB. This subject-matter is especially important in Iran where the annual precipitation level is as low as 250mm. The results showed that the precipitation follows an increasing trend in the region.

Analysis of Features in a Sliding Threshold of Observation for Numeric Evaluation (STONE) Curve

AbstractWe apply idealized scatterplot distributions to the sliding threshold of observation for numeric evaluation (STONE) curve, a new model assessment metric, to examine the relationship between the STONE curve and the underlying point‐spread distribution. The STONE curve is based on the relative operating characteristic (ROC) curve but is developed to work with a continuous‐valued set of observations, sweeping both the observed and modeled event identification threshold simultaneously. This is particularly useful for model predictions of time series data as is the case for much of terrestrial weather and space weather. The identical sweep of both the model and observational thresholds results in changes to both the modeled and observed event states as the quadrant boundaries shift. The changes in a data‐model pair's event status result in nonmonotonic features to appear in the STONE curve when compared to an ROC curve for the same observational and model data sets. Such features reveal characteristics in the underlying distributions of the data and model values. Many idealized data sets were created with known distributions, connecting certain scatterplot features to distinct STONE curve signatures. A comprehensive suite of feature‐signature combinations is presented, including their relationship to several other metrics. It is shown that nonmonotonic features appear if a local spread is more than 0.2 of the full domain or if a local bias is more than half of the local spread. The example of real‐time plasma sheet electron modeling is used to show the usefulness of this technique, especially in combination with other metrics.

On the ROC Area of Ensemble Forecasts for Rare Events

Abstract The relative operating characteristic (ROC) curve is a popular diagnostic tool in forecast verification, with the area under the ROC curve (AUC) used as a verification metric measuring the discrimination ability of a forecast. Along with calibration, discrimination is deemed as a fundamental probabilistic forecast attribute. In particular, in ensemble forecast verification, AUC provides a basis for the comparison of potential predictive skill of competing forecasts. While this approach is straightforward when dealing with forecasts of common events (e.g., probability of precipitation), the AUC interpretation can turn out to be oversimplistic or misleading when focusing on rare events (e.g., precipitation exceeding some warning criterion). How should we interpret AUC of ensemble forecasts when focusing on rare events? How can changes in the way probability forecasts are derived from the ensemble forecast affect AUC results? How can we detect a genuine improvement in terms of predictive skill? Based on verification experiments, a critical eye is cast on the AUC interpretation to answer these questions. As well as the traditional trapezoidal approximation and the well-known binormal fitting model, we discuss a new approach that embraces the concept of imprecise probabilities and relies on the subdivision of the lowest ensemble probability category.

An integrated system for non-destructive detection of shielded nuclear material using multiple modes of passive detection

In this work, an experimental assessment is made of the performance of an integrated system for the detection of shielded nuclear material that features muon tomography and polyvinyl toluene bi-panel detector systems. Experimental setups consisting of a barrel of sand containing a lead flask and/or depleted uranium were defined, and were measured in turn by each detection system. For each system, detection parameters for the presence of depleted uranium were put forward that can be used to integrate the two detection systems in a quantitative manner. In the integrated system, the detectability of differences between setups using a given detection parameter is quantitatively assessed through the use of relative operating characteristic (ROC) curves. Furthermore, in an effort to enhance detection sensitivity, pairs of parameters were combined, through sorting on either maximum or average values, to produce “data fusion” ROC curves. The efficacy of this approach and the complementarity of the detection techniques employed are discussed.

Aviation Turbulence Forecasting over the Portuguese Flight Information Regions: Algorithm and Objective Verification

Aviation turbulence remains one of the leading causes of weather-related aviation accidents. Therefore, turbulence prediction is a major concern of aviation forecasters. This paper describes the turbulence index (TURBIPMA) developed and used operationally at the Portuguese Institute of Sea and Atmosphere (IPMA), based on several diagnostics derived from ECMWF forecasts, using a new calibration approach. The forecast skill of the TURBIPMA and of individual diagnostics are evaluated using turbulence observations over the Portuguese Flight Information Regions and surrounding areas, for 12 months between February 2020 and March 2021 (excluding May and June). The forecasting skill of the predictors is discussed in terms of the Relative Operating Characteristic (ROC) curves, which is widely applied, but also in terms of novel measures such as the Symmetric Extremal Dependence Index (SEDI) and Symmetric Extreme Dependency Score (SEDS). The new measures are particularly relevant in assessing forecasts of rare events, such as moderate-or-greater turbulence. The operational index outperforms individual diagnostics (such as Ellrod) in terms of all verification measures. Furthermore, the use of a new Richardson number function was proven to be beneficial. Finally, the turbulence prediction by IPMA was comparable to that of the London WAFC for one turbulence episode.

Ferroptosis-associated DNA methylation signature predicts overall survival in patients with head and neck squamous cell carcinoma

BackgroundHead and neck squamous cell carcinoma (HNSCC) is a common cancer characterized by late diagnosis and poor prognosis. The aim of this study was to identify a novel ferroptosis-related DNA methylation signature as an alternative diagnosis index for patients with HNSCC.MethodsMethylome and transcriptome data of 499 HNSCC patients, including 275 oral squamous cell carcinoma (OSCC) samples, were obtained from The Cancer Genome Atlas (TCGA). An additional independent methylation dataset of 50 OSCC patients from the NCBI Gene Expression Omnibus (GEO) database was used for validation. As an index of ferroptosis activity, the ferroptosis score (FS) of each patient was inferred from the transcriptome data using single-sample gene set enrichment analysis. Univariate, multivariate, and LASSO Cox regression analyses were used to select CpG sites for the construction of a ferroptosis-related DNA methylation signature for diagnosis of patients.ResultsWe initially inferred the FS of each TCGA HNSCC patient and divided the samples into high- and low-FS subgroups. Results showed that the high-FS subgroup displayed poor overall survival. Moreover, 378 differentially methylated CpG sites (DMCs) were identified between the two HNSCC subgroups, with 16 selected to construct a 16-DNA methylation signature for risk prediction in HNSCC patients using the LASSO and multivariate Cox regression models. Relative operating characteristic (ROC) curve analysis showed great predictive efficiency for 1-, 3-, and 5-year HNSCC survival using the 16-DNA methylation signature. Its predictive efficiency was also observed in OSCC patients from the TCGA and GEO databases. In addition, we found that the signature was associated with the fractions of immune types in the tumor immune microenvironment (TIME), suggesting potential interactions between ferroptosis and TIME in HNSCC progression.ConclusionsWe established a novel ferroptosis-related 16-DNA methylation signature that could be applied as an alternative tool to predict prognosis outcome in patients with HNSCC, including OSCC.

Evaluating skill of BMKG wave model forecast (Wavewatch-3) with observation data in Indian Ocean (5 – 31 December 2017).

Providing Maritime meteorological forecasts (including ocean wave information) is one of BMKG duties. Currently, BMKG employs Wavewatch-3 (WW3) model to forecast ocean waves in Indonesia. Evaluating the wave forecasts is very important to improve the forecasts skill. This paper presents the evaluation of 7-days ahead BMKG’s wave forecast. The evaluation was performed by comparing wave data observation and BMKG wave forecast. The observation data were obtained from RV Mirai 1708 cruise on December 5th to 31st 2017 at the Indian Ocean around 04°14'S and 101°31'E. Some statistical properties and Relative Operating Characteristics (ROC) curve were utilized to assess the model performance. The evaluation processes were carried out on model’s parameters: Significant Wave Height (Hs) and Wind surface for each 7-days forecast started from 00 UTC. The comparation results show that, in average, WW3 forecasts are over-estimate the wave height than that of the observation. The forecast skills determined from the correlation and ROC curves are good for the first- and second-day forecast, while the third until seventh day decrease to fair. This phenomenon is suspected to be caused by the wind data characteristics provided by the Global Forecasts System (GFS) as the input of the model. Nevertheless, although statistical correlation is good for up to 2 days forecast, the average value of Root Mean Square Error (RMSE), absolute bias, and relative error are high. In general, this verifies the overestimate results of the model output and should be taken into consideration to improve BMKG’s wave model performance and forecast accuracy.

Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

PurposeThe present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC).MethodsA total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer–Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan–Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis.ResultsA total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation.ConclusionsThe nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.

Assessing Multi-Criteria Decision Analysis Models for Predicting Groundwater Quality in a River Basin of South India

India is the largest consumer of groundwater in the world, and it suffers from a groundwater crisis due to the overexploitation of groundwater and the deterioration of its quality at an alarming rate. Rapid urbanization, a growing population, and mismanagement are major driving forces behind these groundwater issues. Thus, increasing problems of water scarcity and water-quality deterioration threaten the sustainability of the water supply. This necessitates the development of novel approaches to assess prevailing groundwater quality scenarios at a large scale, which can help protect this vital freshwater resource from contamination. In this study, for the first time, the effectiveness of three Geographical Information System (GIS)-based Multi-Criteria Decision Analysis (MCDA) models (i.e., ‘Unit Weight’, ‘Rank Sum’, and ‘Analytic Hierarchy Process’) was explored for predicting groundwater quality in a river basin of Southern India. The seasonal concentrations of groundwater quality parameters, viz., Cl−, TDS, TH, F−, NO3−-N, Na+, Mg2+, Ca2+, K+, and SO42−, were considered for generating their thematic layers. Each thematic layer was classified into suitable feature classes based on the WHO guidelines for drinking water. The thematic layers and the feature classes of individual groundwater quality parameters were assigned relative weights according to the theories of the three MCDA models mentioned above. These thematic layers were then aggregated in GIS to develop Groundwater Quality Index (GQI) maps of the study area for pre-monsoon and post-monsoon seasons. Furthermore, the accuracy of the developed GQI maps was validated using relative operating characteristic curves. The results of the validation indicated that the GIS-based Analytic Hierarchy Process (AHP) model outperformed with prediction accuracies of 71.4% in the pre-monsoon season and about 85% in the post-monsoon season. However, the performances of the Unit Weight and Rank Sum models were found to be average with prediction accuracies varying from 68% to 63% and 64% to 68%, respectively. Thus, the GIS-based AHP model can serve as a reliable scientific tool for predicting seasonal groundwater quality at a river basin scale. It can be very helpful to the policymakers for devising viable management strategies for groundwater protection as well as for ensuring a sustainable water supply.

Numerical Simulation of Wind Wave Using Ensemble Forecast Wave Model: A Case Study of Typhoon Lingling

A wave forecast numerical simulation was performed for Typhoon Lingling around the Korean Peninsula and in the East Asia region using sea winds from 24 members produced by the Ensemble Prediction System for Global (EPSG) of Korea Meteorological Administration (KMA). Significant wave height was observed by the ocean data buoys used to verify data of the ensemble wave model, and the results of the ensemble members were analyzed through probability verification. The forecast performance for the significant wave height improved by approximately 18% in the root mean square error in the three-day lead time compared to that of the deterministic model, and the difference in performance was particularly distinct towards mid-to-late lead times. The ensemble spread was relatively appropriate, even in the longer lead time, and each ensemble model runs were all stable. As a result of the probability verification, information on the uncertainty that could not be provided in the deterministic model could be obtained. It was found that all the Relative Operating Characteristic (ROC) curves were 0.9 or above, demonstrating good predictive performance, and the ensemble wave model is expected to be useful in identifying and determining hazardous weather conditions.

A theoretical relationship between probabilistic relative operating characteristic skill and deterministic correlation skill in dynamical seasonal climate prediction

Understanding the relationship between probabilistic and deterministic prediction skills is of important significance for the study of seasonal forecasting and verification. Based on the Brier skill score methodology, we have previously found a theoretical relationship between the probabilistic resolution skill and the deterministic correlation (i.e., anomaly correlation; AC) skill and a lack of necessary or consistent relationship between the probabilistic reliability skill and the deterministic skill in dynamical seasonal prediction. Here, we further theoretically investigate the relationship between the probabilistic relative operating characteristic (ROC) skill and the deterministic skill. The ROC measures the discrimination attribute of probabilistic forecast quality, another important attribute besides the resolution and reliability. With some simplified assumptions, we first derive theoretical expressions for the hit and false-alarm rates that are basic ingredients for the ROC curve, then demonstrate a sole dependence of the ROC curve on the AC, and finally analytically derive a relationship between the related ROC score and the AC. Such a theoretically derived ROC-AC relationship is further examined using dynamical models’ ensemble seasonal hindcasts, which is well verified. The finding here along with our previous findings implies that the discrimination and resolution attributes of probabilistic seasonal forecast skill are intrinsically equivalent to the corresponding deterministic skill, while the reliability appears to be the fundamental attribute of the probabilistic skill that differs from the deterministic skill, which constitutes an understanding of the fundamental similarities and difference between the two types of seasonal forecasting skills and predictability and can offer important implications for the study of seasonal forecasting and verification.

Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping

The existence of shallow landslide brings huge threats to the human lives and economic development, as the Lang County, Southeastern Tibet prone to landslide. Landslide susceptibility mapping (LSM) is considered as the key for the prevention of hazard. The primary goal of the present study is to assess and compare four models: classification and regression tree, gradient boosting decision tree (GBDT), adaptive boosting-decision tree and random forest for the performance of landslide susceptibility modeling. Firstly, a landslide inventory map consisting of 229 historical shallow landslide locations was prepared and the same number of non-landslide points was determined by k-means clustering. Secondly, 12 conditioning factors were considered in the landslide susceptibility modeling. The prediction performance of the four models were estimated by fivefold cross validation and relative operating characteristic curve (ROC), area under the ROC curve (AUC) and statistical measures. The results showed that the GBDT performed best in the training and validation dataset, with the highest prediction capability (AUC = 0.986 and 0.940), highest accuracy value (95.3% and 88.1%) and highest kappa index (0.904 and 0.772), respectively. Therefore, the GBDT was considered to be the most suitable model and applied to the whole study area for LSM. The results of this study also demonstrate that the performance can be enhanced with the use of ensemble learning. The sampling strategy of non-landslide points can be improved by combining with clustering analysis which are more reasonable.

Habitat Suitability Analysis of Himalayan Musk Deer (Moschus leucogaster) in Part of Western Himalaya, India

Himalayan musk deer (Moschus leucogaster) is considered endangered by International Union for Conservation of Nature (IUCN) and is under Schedule-I of the Indian Wildlife (Protection) Act, 1972, because of its illicit hunting for musk as well as habitat loss. The present study aims to study the summer range habitat suitability of this important Himalayan ungulate in Kedarnath Wildlife Sanctuary (KWS) of Western Himalaya, India. The study integrated ground presence point locations of the species with satellite data-derived variables in a binomial multiple logistic regression (BMLR) model to analyse its suitability status in KWS. Moist temperate forest (323.38 km2) was found to be the dominant vegetation type, followed by sub-alpine forest (128.24 km2) and alpine pasture (119.51 km2) in KWS. It was found that 7.39% (72.04 km2) of the study area is highly suitable, followed by 14.73% (143.68 km2), 13.43% (131.02 km2) and 64.44% (628.46 km2) area as suitable, moderately suitable and least suitable, respectively, for the species. The results also revealed that 71.59% (51.57 km2) of the highly suitable habitat occurs in sub-alpine forest, 18.41% (13.26 km2) in alpine pasture and 5.63% (4.06 km2) in alpine scrub. It was found that the vegetation type/land use was the most significant variable for habitat suitability assessment of the Himalayan musk deer in KWS. The BMLR model could successfully predict the potential summer range habitat of the species (Relative Operating Characteristics curve = 0.85). The findings of this study could be of immense use in the management of this Wildlife Sanctuary for better protection and conservation of the Himalayan musk deer.

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Landslides are among the most frequent natural hazards in the world. Rainfall is an important triggering factor for landslides and is responsible for topples, slides, and debris flows—three of the most important types of landslides. However, several previous relevant research studies covered general landslides and neglected the rainfall–topples–slides–debris flows disaster chain. Since landslide hazard mapping (LHM) is a critical tool for disaster prevention and mitigation, this study aimed to build a GeoDetector and Bayesian network (BN) model framework for LHM in Shuicheng County, China, to address these geohazards. The GeoDetector model will be used to screen factors, eliminate redundant information, and discuss the interaction between elements, while the BN model will be used for constructing a causality disaster chain network to determine the probability and risk level of the three types of landslides. The practicability of the BN model was confirmed by error rate and scoring rules validation. The prediction accuracy results were tested using overall accuracy, Matthews correlation coefficient, relative operating characteristics curve, and seed cell area index. The proposed framework is demonstrated to be sufficiently accurate to construct the complex LHM. In summary, the combination of the GeoDetector and BN model is very promising for spatial prediction of landslides.