Relative Operating Characteristic Research Articles

A survey-based approach to constructing an abundance index of the pink cusk-eel (Genypterus blacodes) in the fjords of Chilean Patagonia

Chilean Patagonia (41∘00’–57∘00’S) is among the least researched ocean regions in the world but supports commercial fisheries for several fish species, including the pink cusk-eel (Genypterus blacodes). Fishery monitoring of this species in fjords is fragmented, thus limiting the quality and quantity of useful information for stock assessment purposes. We proposed a methodological approach to constructing an abundance index for pink cusk-eel, based on catch per unit of effort surveys in the fjords and inner channels of Patagonia in three administrative regions in Chile (Los Lagos, Aysén, and Magallanes). Information regarding the definition of areas to be sampled and the related operational characteristics were gathered by interviewing fishers. Two research surveys were conducted on board artisanal longline vessels in each administrative region during autumn and spring of 2018. Within each region, 12 fishing grounds were sampled using a standardised protocol for soaking times, number of hooks per line and type of bait, and information was also recorded on target and bycatch species. In the Los Lagos region, the mean catch rate of pink cusk-eel was 3.5 and 4.1 fish/100 hooks on the first and second surveys, respectively. For the Aysén region, the mean catch rate of pink cusk-eel was 1.9 fish/100 hooks in the first survey and 6.6 fish/100 hooks in the second survey. The catch rates of pink cusk-eel on the Magallanes region were low, with only 0.5 fish/100 hooks in the first survey and 0.7 fish/100 hooks in the second survey The main teleost species in the bycatch were tadpole codling (Salilota australis) and rockfish (Helicolenus lengerichi), while the elasmobranch species were dogfish (Schroederichthys chilensis) and yellownose skate (Zearaja chilensis). The mean lengths of pink cusk-eel and tadpole codling showed a latitudinal gradient. This manuscript proposes recommendations and gives insights regarding the design and implementation of surveys over extensive and remote areas with unfavourable weather conditions and where fishery monitoring is fragmented.

Peripheral Blood Inflammatory-Immune Cells as a Predictor of Infertility in Women with Polycystic Ovary Syndrome.

PurposeThis study aimed to investigate the inflammatory-immune cells in the peripheral blood of women with polycystic ovary syndrome (PCOS) and assessed the potential correlation between inflammatory-immune cells and infertility in PCOS women.Materials and MethodsIn this case–control study, the profiles of lymphocyte subsets were analyzed by flow cytometry. White blood cells (WBC), neutrophils (Neu), lymphocytes, Ferriman–Gallwey (F–G) score, testosterone, prolactin, follicle-stimulating hormone, luteinizing hormone, fasting blood glucose, and fasting plasma insulin were measured, together with body mass index. Association between inflammatory-immune cells and PCOS was evaluated. Moreover, inflammatory-immune cells of the PCOS women with infertility were evaluated, and the relative operating characteristic (ROC) curve and cutoff values were calculated.ResultsThe number of WBC, Neu, and lymphocytes was higher in PCOS women than controls (P<0.05). The percentages of total T lymphocytes, CD4+T, and NK were significantly increased in the PCOS group (P<0.001). The CD4/CD8 ratio was obviously elevated for increasing CD4+T (P<0.05). Consequently, T%, CD4+T%, and NK% were found to be the independent risk factors of PCOS by ROC curve and multivariate logistic regression analysis. Furthermore, only NK% was significantly higher in PCOS women with infertility than those who had PCOS without infertility (P<0.001). To diagnose infertility in PCOS, the cutoff value of NK% was calculated as 16.43%.ConclusionThese findings suggest that the pathogenesis of PCOS is related to immune cells including T, CD4+T, and NK cells. NK cells are likely to be a potential predictive factor for PCOS women with infertility.

The STONE Curve: A ROC-Derived Model Performance Assessment Tool.

A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations, that is, the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve—plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero‐intercept unity‐slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data‐model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth's inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.

METODE ANALISIS DISKRIMINAN KUADRAT TERKECIL PARSIAL UNTUK KLASIFIKASI SEGMEN LOYALITAS KONSUMEN SUSU PERTUMBUHAN

Consumer segmentation is the process of dividing consumers into different segments based on consumer characteristics, making it easier for companies to develop marketing strategies. The segmentation is carried out based on consumer loyalty using the RFM (Recency, Frequency, Monetary) approach a number of 7753 members of a nutritional product loyalty program is considered in the analysis. Partial least square discriminant analysis classification modeling is built using the results of consumer segmentation being the a response variable. The model is not good enough based on the AUC (Area Under Curve) value of the ROC (Relative Operating Characteristic) curve that quite low for each segment. The explanatory variables that have high contribution to the model is X5, X9, and X2 with VIP (Variable Importance in the Projection) values more than 1.

Earthquake Forecast Verification for Northwestern Turkey

Seismic events have a pattern of recurrence in magnitude, time and space. Considerable effort is being spent to identify seismic patterns and successfully predict future earthquakes by using the recognized patterns. As a result of these intensive efforts, a variety of methods has been proposed. As the knowledge and experience in the field accumulated in parallel to the variety of the methods proposed, it was deemed necessary to test the performance of some of the highlighted methods, especially considering the wide reception of methods utilizing spatially smoothed seismicity (SSS), pattern informatics (PI) and Relative Intensity (RI). The performance of these methods in the prediction of future earthquakes has been selected for investigation. The investigated area is the region bounded by 270-330 in longitudes and 39.80-410 in latitudes, well known for the North Anatolian Fault. The period of coverage has been selected such as to maximize the length with the minimum magnitude of completeness. As a result of such optimization, the period from 1973 to 2019 has been selected with minimum magnitude of completeness being determined as 3.8. In order to measure the relative performance of the methods, relative operating characteristic (ROC) analysis has been utilized. The method based on SSS has been adapted to the related ROC procedures, while the results of PI and RI methods are already suitable for the evaluation by ROC procedures. After the analysis was completed, according to the ROC procedures, none of the methods were singled out in forecast performance. However, when the ratio of hits versus total alarms and the area covered by the alarms, PI method outperforms two other methods by its efficiency.

An Objective Approach to Generating Multi-Physics Ensemble Precipitation Forecasts Based on the WRF Model

Selecting proper parameterization scheme combinations for a particular application is of great interest to the Weather Research and Forecasting (WRF) model users. This study aims to develop an objective method for identifying a set of scheme combinations to form a multi-physics ensemble suitable for short-range precipitation forecasting in the Greater Beijing area. The ensemble is created by using statistical techniques and some heuristics. An initial sample of 90 scheme combinations was first generated by using Latin hypercube sampling (LHS). Then, after several rounds of screening, a final ensemble of 40 combinations were chosen. The ensemble forecasts generated for both the training and verification cases using these combinations were evaluated based on several verification metrics, including threat score (TS), Brier score (BS), relative operating characteristics (ROC), and ranked probability score (RPS). The results show that TS of the final ensemble improved by 9%-33% over that of the initial ensemble. The reliability was improved for rain ≤ 10 mm day-1, but decreased slightly for rain > 10 mm day-1 due to insufficient samples. The resolution remained about the same. The final ensemble forecasts were better than that generated from randomly sampled scheme combinations. These results suggest that the proposed approach is an effective way to select a multi-physics ensemble for generating accurate and reliable forecasts.

Improvements in tropical cyclone forecasting through ensemble prediction system at NCMRWF in India

This paper deals with the comparison of cyclone forecasts from the two versions of the operational global ensemble prediction system (EPS) at the National Centre for Medium Range Weather Forecasting (NEPS). The previous version had a horizontal resolution of 33 km with 44 ensemble members (NEPS) whereas the updated version of this EPS has a resolution of 12 km with 11 members (NEPS-UP). The ensemble mean forecasts from both the models are compared using the direct position (DPE), along (ATE) and cross track (CTE) errors. For the verification of strike probability, Brier Score (BS), Brier Skill Score (BSS), Reliability Diagram, Relative Operating Characteristic (ROC) Curve and Root Mean Square Error (RMSE) in mean Vs Spread in members are used. For verification of intensity, RMSE in maximum wind speed from the ensemble mean forecasts are compared.Comparison of ensemble mean tracks from both models showed lower errors in NEPS-UP for all forecast lead times. The decrease in the DPE, ATE and CTE in NEPS-UP was around 38%, 48% and 15% respectively. NEPS-UP showed lower BS and higher BSS values indicating a better match between observed frequencies and forecast probabilities as well as higher prediction skills. The reliability diagram showed higher accuracy for NEPS-UP as compared to NEPS. The ROC curves showed that for forecasts with higher probabilities the hit rate was high in NEPS-UP. There was a greater consensus between the RMSE and Spread for NEPS-UP at all lead times. It was also seen that the RMSE in mean showed a 41% decrease from NEPS to NEPS-UP. On comparing maximum wind, it was found that for all lead times the RMSE in maximum wind speed for NEPS-UP was lower than NEPS.

A GIS-based approach to explore the factors contributing towards Urban residential land development and re-development (LDR): a case of Rajshahi City Corporation area

ABSTRACT Urban Residential land development and redevelopment intensity (LDR) act as an essential index to understand magnitude of urban livability and sustainable development. Recent urbanization pattern of Rajshahi city has made a rising demand for land, which made it a precious commodity. As a result of it, Rajshahi City Corporation (RCC) area is undergoing significant residential LDR in recent years. The understanding of factors influencing this residential LDR has great importance in real estate development as well as overall housing situation of thecity. For this purpose, this study tries to explore the spatial characteristics of LDR. Relative Operating Characteristics (ROC) test statistics (>0.5) with improve hedonic model are set out to identify the internal drivers of LDR. Finally, geostatistics is explored by integrating methods of Geographic Information System (GIS) and SPSS regression analysis to analyze the correlation between LDR and driving factors. The spatial distribution of residential LDR has shown a strong distance decline law. One of the most exciting findings was that increase in distance to Central Business District (CBD) from the residential land has a slight influence on LDR. The outcomes of the paper will be beneficial for investors and policy-makers in their development decision-making processes.

A Comparative Study of the Forest Fire Danger Index Calculation Methods Using Backpropagation

Forest fires are one of the threats of disasters in Indonesia. Rising earth temperatures add to the higher potential contribution of forest fires. Many forest fire hazard index calculation methods have been developed to analyze the impact categories arising from changes in meteorological parameters. Each calculation method has advantages in calculating the magnitude of the potential. One method of calculating the forest fire hazard index is needed from many choices of methods that have the highest level of accuracy to predict the potential for a fire. A comparative study of the methods will be validated and guide the best calculating procedure to be implemented for the prediction. Predictions that have excellent accuracy and precision can be used as an early warning system. This study will predict forest fires for each calculation method using the Backpropagation algorithm, then analyze the accuracy of the prediction results using Relative Operating Characteristics (ROC). The methods compared include the methods that have been used in Indonesia as a country that has tropical rainforests, namely Keetch-Byram Drought Index (KBDI), Standard Precipitation Index (SPI), McArthur Forest Fire Danger Index (MFFDI), and Fire Weather Index (FWI). Through a comparative study of this calculation model, it is concluded that MFFDI is the best method of calculating the fire hazard index with an accuracy value of 0.917 and a precision value of 0.667.

Ensemble forecasting system for the management of the Senegal River discharge: application upstream the Manantali dam

Providing useful inflow forecasts of the Manantali dam is critical for zonal consumption and agricultural water supply, power production, flood and drought control and management (Shin et al., Meteorol Appl 27:e1827, 2019). Probabilistic approaches through ensemble forecasting systems are often used to provide more rational and useful hydrological information. This paper aims at implementing an ensemble forecasting system at the Senegal River upper the Manantali dam. Rainfall ensemble is obtained through harmonic analysis and an ARIMA stochastic process. Cyclical errors that are within rainfall cyclical behavior from the stochastic modeling are settled and processed using multivariate statistic tools to dress a rainfall ensemble forecast. The rainfall ensemble is used as input to run the HBV-light to product streamflow ensemble forecasts. A number of 61 forecasted rainfall time series are then used to run already calibrated hydrological model to produce hydrological ensemble forecasts called raw ensemble. In addition, the affine kernel dressing method is applied to the raw ensemble to obtain another ensemble. Both ensembles are evaluated using on the one hand deterministic verifications such the linear correlation, the mean error, the mean absolute error and the root-mean-squared error, and on the other hand, probabilistic scores (Brier score, rank probability score and continuous rank probability score) and diagrams (attribute diagram and relative operating characteristics curve). Results are satisfactory as at deterministic than probabilistic scale, particularly considering reliability, resolution and skill of the systems. For both ensembles, correlation between the averages of the members and corresponding observations is about 0.871. In addition, the dressing method globally improved the performances of ensemble forecasting system. Thus, both schemes system can help decision maker of the Manantali dam in water resources management.

Calibration Of Rainfall Ensemble Prediction Of ECMWF System 4 Using Bayesian Model Averaging

Bayesian model averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensembles in the form of predictive PDFs. It is known that BMA is able to improve the reliability of probabilistic forecast of short range and medium range rainfall forecast. This study aims to develop the application of BMA to calibrate seasonal forecast (long range) in order to improved quality of seasonal forecast in Indonesia. The seasonal forecast used is monthly rainfall from the output of the ensemble prediction system European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4) and it is calibrated against observational data at 26 stations of the Agency for Meteorology Climatology and Geophysics of Republic of Indonesia (BMKG) in Java Island in 1981 – 2018. BMA predictive PDFs is generated with Gamma distribution approach which is obtained based on sequential training windows (JTS) and conditionals training windows (JTC). BMA-JTS approach is done by selecting the width of the 30-month training window as the optimal training period while the BMA-JTC is carried out with a cross-validation scheme for each month. In general, Both of BMA-JTS and BMA-JTC better than RAW models. BMA-JTC calibration results are varying according to spatial and temporal, but in general the result is better in the dry season and during the El Nino phase. BMA is able to improve the distribution characteristics of the RAW model ECS4 prediction which is shown by: a smaller value of Continuous Rank Probability Score (CRPS), a larger value of the Continuous Rank Probability Skill Score (CRPSS) and more flat form of the Verification Rank Histogram (VRH) than the RAW model. BMA also increases the skill, esolution and reliability of prediction of probability Below Normal (BN) and Above Normal (AN), which is known from the increasing Brier Skill Score (BSS), and the increasing area under curve of Relative Operating Characteristics (ROC) compared to the RAW model. Furthermore, the reliability of BN and AN of BMA results also has the category of “still very useful” and “perfect” compared to RAW models that are in the “dangerous”, “not useful” and “marginally useful” categories. The reliability of BMA results with the category “still very useful” and “perfect” show that the probabilistic forecast of BN and AN events can be used in making decisions related to seasonal forecast.

Probabilistic Heat Wave Forecast Based on a Large-Scale Circulation Pattern Using the TIGGE Data

Abstract Forecasting extreme events is important for having more time to prepare and mitigate high-impact events because those are expected to become more frequent, intense, and persistent around the globe in the future under the warming atmosphere. This study evaluates the probabilistic predictability of the heat wave index (HWI) associated with large-scale circulation patterns for predicting heat waves over South Korea. The HWI, reflecting heat waves over South Korea, was defined as the vorticity difference at 200 hPa between the South China Sea and northeast Asia. The forecast of up to 15 days from five ensemble prediction systems and the multimodel ensemble has been used to predict the probabilistic HWI during the summers of 2011–15. The ensemble prediction systems consist of different five operational centers, and the forecast skill of the probability of heat waves occurrence was assessed using the Brier skill score (BSS), relative operating characteristics (ROC), and reliability diagram. It was found that the multimodel ensemble is capable of better predicting the large-scale circulation patterns leading to heat waves over South Korea than any other single ensemble system through all forecast lead times. We concluded that the probabilistic forecast of the HWI has promise as a tool to take appropriate and timely actions to minimize the loss of lives and properties from imminent heat waves.

Analysing and modelling the relationship between air freight movement and airport characteristics in India

Air freight transportation is paramount for the expansion of trade and economic growth of any country. Recent statistics indicates that developing countries like India, are showing continuous growth trend in air freight sector; however, the modelling efforts for the air freight are scarce as compared to other freight mode. This paper analysed the relationship between air freight demand and airport characteristics using the aggregate data collected from the 42 airports around India. A set of statistical models were developed using regression analyses to estimate the air freight tonnage in an airport. The models are designed by considering their practical applications in facility planning and investment decision for airport infrastructure. Three different types of determinants were identified related city, physical, and operational characteristics of the airports. The results showed that number of industrial units in the city is a key determinant in predicting the air freight demand followed by population. On the other hand, physical and operational characteristics such as runway length, building area, passenger volume, and aircraft movement showed better correlation with freight activities. Another important finding is that the air freight demand will significantly change based on the city type in which the airport is located. Comparison of modelling results using different approaches revealed that that panel data regression models using airport’s operational characteristics gives better presentation of air-freight movement. Overall, the air freight demand models discussed in this paper may be utilized in facility planning, policy effect analysis and access road improvements of airports in India.

Verification of high resolution (12 km) Global Ensemble Prediction System

Abstract Ensemble Prediction Systems (EPSs) are used to estimate the uncertainty in a weather forecast as well as the most likely outcome. To improve weather forecasts and services, the global EPS at the NCMRWF has been upgraded to 12 km horizontal resolution (with 22 perturbed +1 control members). Although the upgraded version of EPS had become operational from 1st June 2018 in the newly acquired high-performance computing system (HPCS), the previous EPS at 33 km horizontal resolution (with 44 perturbed +1 control members) was also running in the old HPCS till 16th July 2018. As compared to the old EPS in NCMRWF, the new EPS includes perturbations in initial state of sea surface temperature, soil moisture content and deep soil temperature. The analysis of the new EPS also includes some more sources of observations. The perturbed 22 members long forecast of the new EPS provided at 00 UTC is the combination of 11 members from 00 UTC cycle and lagged 11 members from 12 UTC cycle of previous day. In the present study, the performances of the new and old EPS are compared for the period from 1st June to 16th July 2018. Performances of both the systems are assessed considering different attributes of probabilistic forecasting. The verification of precipitation, geopotential height at 500 hPa and zonal wind at 850 hPa has been carried out over the northern hemisphere, southern hemisphere and a smaller tropical region which includes India. The metrics used for verification are ensemble-spread, ensemble root-mean-square error, brier score, brier skill score, relative operating characteristics, reliability, sharpness diagrams, rank histogram, ranked probability score, continuous ranked probability score and continuous ranked probability skill score. Two heavy rainfall events have also been considered for subjective comparison of probabilistic prediction capabilities of both the systems. The results show that the new EPS is more skilful in probabilistic forecasts especially over the Indian region and it significantly outperforms the old EPS in probabilistic forecasts of the selected extreme weather events.

Forecast Customization System (FOCUS): A Multimodel Ensemble-Based Seasonal Climate Forecasting Tool for the Homogeneous Climate Zones of Myanmar

A Multi-Model Ensemble (MME) based seasonal rainfall forecast customization tool called FOCUS was developed for Myanmar in order to provide improved seasonal rainfall forecast to the country. The tool was developed using hindcast data from 7 Global Climate Models (GCMs) and observed rainfall data from 49 meteorological surface observatories for the period of 1982 to 2011 from the Department of Meteorology and Hydrology. Based on the homogeneity in terms of the rainfall received annually, the country was divided into six climatological zones. Three different operational MME techniques, namely, (a) arithmetic mean (AM-MME), (b) weighted average (WA-MME), and (c) supervised principal component regression (PCR-MME), were used and built-in to the tool developed. For this study, all 7 GCMs were initialized with forecast data of May month to predict the rainfall during June to September (JJAS) period, which is the predominant rainfall season for Myanmar. The predictability of raw GCMs, bias-corrected GCMs, and the MMEs was evaluated using RMSE, correlation coefficients, and standard deviations. The probabilistic forecasts for the terciles were also evaluated using the relative operating characteristics (ROC) scores, to quantify the uncertainty in the GCMs. The results suggested that MME forecasts have shown improved performance (RMSE = 1.29), compared to the raw individual models (ECMWF, which is comparatively better among the selected models) with RMSE = 4.4 and bias-corrected RMSE = 4.3, over Myanmar. Specifically, WA-MME (CC = 0.64) and PCR-MME (CC = 0.68) methods have shown significant improvement in the high rainfall (delta) zone compared with WA-MME (CC = 0.57) and PCR-MME (CC = 0.56) techniques for the southern zone. The PCR method suggests higher predictability skill for the upper tercile (ROC = 0.78) and lower tercile categories (ROC = 0.85) for the delta region and is less skillful over lower rainfall zones like dry zones with ROC = 0.6 and 0.63 for upper and lower terciles, respectively. The model is thus suggested to perform relatively well over the higher rainfall (Wet) zones compared to the lower (Dry) zone during the JJAS period.

Modeling spatial pattern of dengue in North Central Mexico using survey data and logistic regression

ABSTRACT Dengue is a major public health concern mainly in tropical and subtropical environments worldwide. Despite several attempts to prevent this disease occurring in tropical regions of Mexico, it has not yet been controlled. This work focused on spatial modeling of confirmed dengue fever cases that occurred during the period 2010–2014 in the Huasteca Potosina region of Mexico. Multivariable Logistic Regression Modeling (MLRM) was used to determine the relationship between explanatory variables and the presence/absence of dengue. Model performance was evaluated using the area under curve (AUC) of the relative operating characteristic (ROC); AUC > 0.95. A high spatial resolution map was created to reveal the most probable patterns of dengue risk. Our results can be used for targeted control and prevention programs at local and regional levels. This methodology can be applied to other major diseases that are spatially distributed in accordance with environmental factors.

Application of three statistical models for predicting the risk of diabetes

BackgroundAt present, the proportion of undiagnosed diabetes in Chinese adults is as high as 15.5%. People with diabetes who are not treated and controlled in time may have various complications, such as cardiovascular and cerebrovascular diseases and diabetic foot disorders, which not only seriously affect the quality of life of people with diabetes but also impose a heavy burden on families and society. Therefore, prevention and control of type 2 diabetes is of great significance.MethodsWe constructed a logistic regression model, a neural network model and a decision tree model to analyse the risk factors for type 2 diabetes and then compared the prediction accuracy of the different models by calculating the area under the relative operating characteristic (ROC) curve and back-inputting the data into the model.ResultsThe prevalence of type 2 diabetes in 4177 subjects who were not diagnosed with type 2 diabetes was 9.31%. The most influential factors associated with type 2 diabetes were triglyceride (TG) ≥ 1.17 mmol/L (odds ratio (OR) =2.233), age ≥ 70 years (OR = 1.734), hypertension (OR = 1.703), alcohol consumption (OR = 1.674), and total cholesterol≥5.2 mmol/L (TC) (OR = 1.463). The prediction accuracies of the three prediction models were 90.8, 91.2, and 90.7%, respectively, and the areas under curve (AUCs) were 0.711, 0.780, and 0.698, respectively. The differences in the AUCs after back propagation (BP) of the neural network model, logistic regression model and decision tree model were statistically significant (P < 0.05).ConclusionBP neural networks have a higher predictive power for identifying the associated risk factors of type 2 diabetes than the other two models, but it is necessary to select a suitable model for specific situations.

Assessing the Potential Future Forest-Cover Change in Romania, Predicted Using a Scenario-Based Modelling

Forest-cover dynamics is of wide concern due to its role in climate change, biodiversity losses, water balance and land degradation, as well as social and economic development. Hence, exploring land-use/cover dynamic is important in order to improve our understanding of the causes of forest-cover change and to detect the future trend. Furthermore, projecting a future land-use/cover pattern can help identifying potential areas where forest-cover change will occur in the future and the potential consequences of these processes in order to improve land-use planning and policies. Similar to other East European countries, Romania is experiencing rapid land-use/cover changes after the breakdown of socialism; a clear trend was registered by deforestation, which reflects the consequences of a continuous forests dynamics and little environmental care. Consequently, this study, carried out in order to analyse the potential future cover-change, resulted in the land-use/cover scenario (2007–2050) simulated using CLUE-S (the Conversion of Land Use and its Effects at Small regional extent) modelling framework, applied to development regions in Romania. Overall, the model results in different spatial patterns of land-use/cover change, projecting a slight increase in the forest-cover area of about 82,000 ha. Furthermore, the model simulated widespread deforestation, mainly in relation to agricultural land expansion. The area under the curve (AUC) for the relative operating characteristic (ROC) and the Kappa simulation (KSimulation) were used to assess the predictive power of the determinant factors included and to evaluate the spatial performance of the model. The obtained ROC/AUC values (0.83–0.88) indicate the great power of the determinant factors to explain the forest-cover pattern in the area. Furthermore, the KSimulation scores (0.69–0.79) highlight the potential of the CLUE-S model to simulate future forest-cover change in relation to the other land-use/cover categories. The results can provide useful inputs for effective forest resource management and environmental policies. Moreover, the spatial data obtained can contribute to exploring future potential environmental implications (e.g. assessing landslide and flood hazard scenarios, forest biomass dynamics and their impact on carbon allocation, or the impact of forest-cover change on ecosystem services).

The prediction of rainfall events using WRF (weather research and forecasting) model with ensemble technique

The research of ensemble technique has become popular in recent years, which can produce probabilistic predictions of rainfall on a short or medium scale. Prediction ensemble is a model formed by combining the predictive system model; the models used was member. This study aims to analyse the ability of the ensemble method in predicting rainfall events in Cilacap (07°45’ S, 109°02’ E) and Denpasar (08°37’ S, 115°13’ E). Data processing used ensemble equation; its ensemble member consisted of four members of running model WRF-ARW every cycle (00, 06, 12 and 18 UTC). In general, during the period of July 1 to 20 and February 1 to 20, 2016, the results of the models both single model and ensemble technique show that the model underestimate in predicting rainfall. Nevertheless, the single and three models of ensemble prediction methods estimated dry conditions very well but failed to predict the event of heavy rain. There were supported by the plot curve of the Relative Operating Characteristic (ROC) curve as a completely indicating that the three ensemble techniques performed the same results in all regions, none of them gave many different predictions between ensemble techniques and other ensemble techniques. The usual weighted or ensemble mean method produces almost identical rainfall predictions.

Identifying the most important spatially distributed variables for explaining land use patterns in a rural lowland catchment in Germany

Land use patterns arise from interactive processes between the physical environment and anthropogenic activities. While land use patterns and the associated explanatory variables have often been analyzed on the large scale, this study aims to determine the most important variables for explaining land use patterns in the 50 km2 catchment of the Kielstau, Germany, which is dominated by agricultural land use. A set of spatially distributed variables including topography, soil properties, socioeconomic variables, and landscape indices are exploited to set up logistic regression models for the land use map of 2017 with detailed agricultural classes. Spatial validation indicates a reasonable performance as the relative operating characteristic (ROC) ranges between 0.73 and 0.97 for all land use classes except for corn (ROC = 0.68). The robustness of the models in time is confirmed by the temporal validation for which the ROC values are on the same level (maximum deviation 0.1). Non-agricultural land use is generally better explained than agricultural land use. The most important variables are the share of drained area, distance to protected areas, population density, and patch fractal dimension. These variables can either be linked to agriculture or the river course of the Kielstau.