Probability Density Function Curves Research Articles

Assessment of Destructive and Nondestructive Analysis for GGBS Based Geopolymer Concrete and Its Statistical Analysis.

Geopolymer is the alternative to current construction material trends. In this paper, an attempt is made to produce a sustainable construction composite material using geopolymer. Ground granulated blast furnace slag (GGBS)-based geopolymer concrete was prepared and tested for different alkaline to binder ratios (A/B). The effect of various temperatures on compressive strength properties was assessed. The cubes were exposed to temperature ranging from 50 to 70 °C for a duration ranging from 2 to 10 h, and the compressive strength of the specimens was analyzed for destructive and non-destructive analysis and tested for 7, 28, and 90 days. The obtained compressive strength (CS) results were analyzed employing the probability plot (PP) curve, distribution overview curve (DOC), probability density function (PDF), Weibull, survival, and hazard function curve. Maximum compressive strength was achieved for the temperature of 70 °C and an A/B of 0.45 for destructive tests and non-destructive tests with 44.6 MPa and 43.56 MPa, respectively, on 90 days of testing. The survival and hazard function curves showed incremental distribution characteristics for 28 and 90 days of testing results with a probability factor ranging from 0.8 to 1.0.

Evaluation of the raindrop size distribution representation of microphysics schemes in typhoon lekima using disdrometer network observations

Raindrop size distribution (RSD) is a crucial linkage between the precipitation characteristics and related microphysical processes. However, its representation in the numerical weather model still has significant uncertainties. In this study, by using the Weather Research and Forecasting (WRF) model with three widely-used bulk microphysics schemes: The Morrison, Thompson, and WRF double-moment 6-class (WDM6) microphysics schemes, the simulated RSD characteristics in the eyewall and spiral rainbands of Typhoon Lekima (2019) are evaluated against a dense disdrometer network observation. Verifications generally show the overestimated mass-weight mean diameter (Dm) but the underestimated normalized number concentration (Nw) by three microphysics schemes. In comparison with the other two schemes, the RSD characteristics simulated by the Thompson scheme are more consistent with the observation. Furthermore, simulations produced less and somewhat different RSD variability among the eyewall and rainbands than observations. From the outer rainband to the eyewall, the probability density function (PDF) curve of observed Dm shows a left-skewed distribution with a flatter shape and decreasing peakedness, whereas the simulations show a narrower and right-skewed PDF of Dm. The discrepancy between the model and observations is primarily attributed to more intense tendencies of Dm growth and Nw reduction in simulations during raindrops descend to the ground. Examinations of vertical profiles for rain-related microphysical processes indicate that this discrepancy likely originates with the over-predicted collision-coalescence process.

Assessment of the Flood and Drought Occurrence Using Statistically Downscaled Local Climate Models: A Case Study in Langat River Basin, Malaysia

Abstract Climate change is a complex and multi-layer issue with global and local entanglement. In this study, Langat River Basin is chosen. Secondary data was used including the historical flood and drought event reports, Standardized Precipitation Index-1 data and Canadian Earth System Model (CanESM2) along with Australian Community Climate and Earth System Simulator Coupled Model (ACCESS CM-2). These data were used to determine the monthly flood and drought precipitation risk based on five regions of Langat River Basin. The CanESM2 and ACCESS CM-2 based on RCP 4.5 and RCP 8.5 scenarios were downscaled and bias corrected for this study. The reliability of these models was then analyzed with Pearson correlation and probability density function (PDF). The future flood and drought risks from year 2020 to 2100 were predicted using the most reliable local climate model local climate hazard thresholds. The CanESM2 RCP 4.5 scenario was identified to have moderate relationship with the historical precipitation trend in Langat River Basin. The Pearson correlation outcomes were then verified by analyzing the PDF curve between models and historical precipitation. The result shows that the downscaled CanESM2 RCP 4.5 was determined to have moderate correlation r = 0.30, whereas highest similarity with the historical precipitation trend 98.63 % based on 2006 to 2018 period. The scenario is consistent with medium emission coupled with increasing mitigation efforts in Malaysia. Furthermore, the flood and drought risk assessment outcomes show that the occurrence rate for Central, Northern, Southern, Western, and Eastern region in Langat River Basin were determined as 41.97 %, 60.19 %, 40.23 %, 20.16 %, and 34.98% respectively. The Central region was predicted having two drought incidences (February 2069 and February 2099) due to extreme dry season predicted based on 2020 to 2100 period.

Interval forecasting for urban water demand using PSO optimized KDE distribution and LSTM neural networks

The current literature on water demand forecasting mostly focuses on giving accurate point predictions of water demand. However, the water demand point forecasting will encounter uninformative and unreliable problems when the uncertainty level of data increases. To solve the above problem, a hybrid model (KDE-PSO-LSTM), which combines long short-term memory networks (LSTM) to kernel density estimation (KDE) optimized by using the particle swarm optimization (PSO) algorithm, is proposed to acquire the water demand prediction interval (PI) to quantify the likely uncertainties in the predictions. At first, the prediction errors are obtained by the difference between the real values of water demand and the predictive values based on the LSTM model. Then, a novel splitting strategy is proposed to divided point predictions into different levels to deal with the problem that it is difficult to fit the prediction errors of the whole water demand using a single probability density function (PDF). Next, the PSO is used to optimize the hyper-parameter of the KDE method for fitting the PDF curves of different levels prediction errors. Moreover, due to the irregular distribution of prediction errors, a search method called confidence-window shifting is presented to determine the optimal prediction error interval from the fitted PDF curves. After that, the upper bounds and the lower bounds of the best intervals of prediction errors are added to the point predictions to attain the final PI of urban water demand. Finally, to demonstrate the superiorities of the proposed model, the proposed KDE-PSO distribution is compared to other well-known distributions, i.e, the KDE distribution, the Beta-PSO distribution and the normal distribution. The experimental results show that the comprehensive performances of the PIs generated from the proposed KDE-PSO-LSTM model are better than that of KDE-PSO-BP, KDE-PSO-RNN, ND-LSTM, KDE-LSTM, Beta-PSO-LSTM and KDE-GA-LSTM. Therefore, it can be demonstrated that the KDE-PSO-LSTM model can provide reliable decision support to policy-makers for making the optimal water supplying management.

A probability distribution model based on the maximum entropy principle for wind power fluctuation estimation

Volatility is an inherent characteristic of wind power. The normal, Weibull, and other distributions are commonly used to describe the characteristics of wind power. However, many of these single distribution models are not suitable to characterize wind power, particularly those converging from multiple wind farm groups. In this study, the maximum entropy distribution model is utilized as probability density function to fit wind power fluctuations. To evaluate the effectiveness and performance of the model, we compared the probability density function curve and cumulative distribution function curve fitting with the mixed Gaussian, the mixed logistic, and the mixed Weibull distribution models using simulation experiments. We developed four scenarios with different numbers of wind farm groups and calculated the fitting error, determination coefficient, and K–S test value for the four models. The simulation results show that the maximum entropy distribution model has the best fitting effect in all four scenarios. Compared with the other three distribution models, the maximum entropy distribution model has clear advantages and is more suitable to describe the fluctuation characteristics of wind power. The parameter estimation of the maximum entropy distribution model can be transformed into a linear optimization problem to significantly reduce calculation complexity.

A Novel Nomogram for Prediction and Evaluation of Lymphatic Metastasis in Patients With Renal Cell Carcinoma.

BackgroundLymphatic metastasis is an important mechanism of renal cell carcinoma (RCC) dissemination and is an indicator of poor prognosis. Therefore, we aimed to identify predictors of lymphatic metastases (LMs) in RCC patients and to develop a new nomogram to assess the risk of LMs.MethodsThis study included patients with RCC from 2010 to 2018 in the Surveillance, Epidemiology, and Final Results (SEER) database into the training cohort and included the RCC patients diagnosed during the same period in the Second Affiliated Hospital of Dalian Medical University into the validation cohort. Univariate and multivariate logistic regression analysis were performed to identify risk factors for LM, constructing a nomogram. The receiver operating characteristic (ROC) curves were generated to assess the nomogram’s performance, and the concordance index (C-index), area under curve value (AUC), and calibration plots were used to evaluate the discrimination and calibration of the nomogram. The nomogram’s clinical performance was evaluated by decision curve analysis (DCA), probability density function (PDF) and clinical utility curve (CUC). Furthermore, Kaplan-Meier curves were performed in the training and the validation cohort to evaluate the survival risk of the patients with lymphatic metastasis or not. Additionally, on the basis of the constructed nomogram, we obtained a convenient and intuitive network calculator.ResultsA total of 41837 patients were included for analysis, including 41,018 in the training group and 819 in the validation group. Eleven risk factors were considered as predictor variables in the nomogram. The nomogram displayed excellent discrimination power, with AUC both reached 0.916 in the training group (95% confidence interval (CI) 0.913 to 0.918) and the validation group (95% CI 0.895 to 0.934). The calibration curves presented that the nomogram-based prediction had good consistency with practical application. Moreover, Kaplan-Meier curves analysis showed that RCC patients with LMs had worse survival outcomes compared with patients without LMs.ConclusionsThe nomogram and web calculator (https://liwenle0910.shinyapps.io/DynNomapp/) may be a useful tool to quantify the risk of LMs in patients with RCC, which may provide guidance for clinicians, such as identifying high-risk patients, performing surgery, and establishing personalized treatment as soon as possible.

Wake waves of a planing boat: An experimental model

The wake waves generated by the steady movement of a planing hull are analyzed by means of towing tank tests. Two sets of waves, including divergent and transverse waves, are identified and then analyzed. The wave period of the divergent waves is seen to decrease by the increase in speed of the vessel. These waves are seen to damp temporally. The mechanisms that lead to damping of the divergent wave were found to depend on the wave orbital Reynolds number in semi-planing regime, though that of in-planing regime is a function of the Reynolds number of the boat. The wake angle is seen to decrease with the increase in Froude number, the rate of which becomes relatively large in-planing regime. Transverse waves are captured through measurements, and it is shown that while their period is longer than those of the divergent waves, they are not noticeably damped. Throughout the spectral analysis, it is demonstrated that divergent waves reach a higher level of nonlinearity by the increase in Froude number and, hence, the wave energy is distributed over a boarder range of frequency. The height of the transverse wave is observed to become lower by the increase in speed, but as the towing speed increases, the probability density function curves of surface elevation deviate more and more from the Gaussian distribution.

Efficient phase-shift error compensation algorithm based on probability density function

In non-digital projection systems, the inaccuracy of phase shifts is one of the most important nonlinear errors. In this paper, an efficient and universal compensation algorithm is proposed, which does not need to find the exact phase shift amounts, but to compensate the periodical errors by a universal two-coefficient error model. With this model, only two error coefficients should to be searched for any multi-step phase shift algorithm. To find the error coefficients, a correlation process and a tool known as the probability density function (PDF) are used. First, a set of pre-simulated PDF curves are generated with the different error coefficients. Second, a practical PDF curve from captured fringes is calculated. Finally, the correlation process is carried out to locate the most similar simulated PDF curve, whose error coefficients are the practical ones. In this paper, we demonstrate the compensation for three, four and five-step phase-shifting algorithms separately. Experimental results show that this method has high universality, and can meet the requirements of rapid and high accuracy measurement.

Gamma distribution model of diffusion MRI for evaluating the isocitrate dehydrogenase mutation status of glioblastomas.

To determine whether the γ distribution (GD) model of diffusion MRI is useful in the evaluation of the isocitrate dehydrogenase (IDH) mutation status of glioblastomas. 12 patients with IDH-mutant glioblastomas and 54 patients with IDH-wildtype glioblastomas were imaged with diffusion-weighted imaging using 13 b-values from 0 to 1000 s/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) < 1.0×10-3 mm2/s; f2, D > 1.0×10-3 and <3.0×10-3 mm2/s; f3, D > 3.0 × 10-3 mm2/s. The GD model-derived parameters measured in gadolinium-enhancing lesions were compared between the IDH-mutant and IDH-wildtype groups. Receiver operating curve analyses were performed to assess the parameters' diagnostic performances. The IDH-mutant group's f1 (0.474 ± 0.143) was significantly larger than the IDH-wildtype group's (0.347 ± 0.122, p = 0.0024). The IDH-mutant group's f2 (0.417 ± 0.131) was significantly smaller than the IDH-wildtype group's (0.504 ± 0.126, p = 0.036). The IDH-mutant group's f3 (0.109 ± 0.060) was significantly smaller than the IDH-wildtype group's (0.149 ± 0.063, p = 0.0466). The f1 showed the best diagnostic performance among the GD model-derived parameters with the area under the curve value of 0.753. The GD model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and was useful in the differentiation of these tumors. Diffusion MRI based on the γ distribution model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and its use enabled the significant differentiation of these tumors. The γ distribution model may contribute to the non-invasive identification of the IDH mutation status based on histological viewpoint.

Parametric Random Vibration Analysis of an Axially Moving Laminated Shape Memory Alloy Beam Based on Monte Carlo Simulation.

The study of the bifurcation, random vibration, chaotic dynamics, and control of laminated composite beams are research hotspots. In this paper, the parametric random vibration of an axially moving laminated shape memory alloy (SMA) beam was investigated. In light of the Timoshenko beam theory and taking into consideration axial motion effects and axial forces, a random dynamic equation of laminated SMA beams was deduced. The Falk’s polynomial constitutive model of SMA was used to simulate the nonlinear random dynamic behavior of the laminated beam. Additionally, the numerical of the probability density function and power spectral density curves was obtained through the Monte Carlo simulation. The results indicated that the large amplitude vibration character of the beam can be caused by random perturbation on axial velocity.

Oscylacje klimatyczne w zakresie 20 000–11 000 cal BP zapisane w utworach formacji Piaski i Szerokie w odkrywkach Kopalni Węgla Brunatnego Bełchatów: spojrzenie poprzez analizę dat radiowęglowych

Radiocarbon dates of organic sediments, taken from the soil horizon, gyttja, peats, plant remains and mineral-organic sediments – silts and silty-sandy sediments with admixture of organic material – were collected and analysed. Age determinations were made for samples collected over a number of years from the upper part of sediments of the Piaski and Szerokie Formations in the Bełchatów Lignite Open Cast Mine (28 dates) and their surroundings (2 dates) (Fig. 1; Tab. 1). The organic sediments analysed are mainly associated with the valleys of the Krasówka, Struga Żłobnicka, Świętojanka and Widawka Rivers. They were formed within alluvia (probably fillings of oxbow lakes). Some of the organic deposits were formed in the vicinity of dunes or aeolian covers (mid-dune depressions). During the Late Glacial in this area, aggradation dominated the river valleys. Fluvial processes were accompanied by aeolian processes. A large proportion of aeolian grains is characteristic of the upper part of the profile of the sands formations. Despite the filling of the valleys with fluvio-aeolian sediments, fluvial erosion also occurred in places. The Folwark 1 and 2 sites in the Struga Żłobnicka River valley yielded 12 age determinations (from 14 810±170 to 11 130±120 BP, i.e. 18 270–12 900 cal BP). In the Widawka valley, the sites Bełchatów 3/88 BM, JG, I/83/JG, II/83/JG, Buczyna 8, Rogowiec 1 and Wola Grzymalina yielded 12 dates falling in the pe-riod 14 350±580 to 10 160±140 BP, i.e. 18 240––11 400 cal BP. In the Kleszczów 3/90 site, only one date of 11 460±120 BP (13 460–13 190 cal BP) related to peat in the Świętojanka River valley. The last two determinations, archived as site unknown and Kuców C–IIa, are dated 15 850±180 (19 360–18 930 cal BP) and 13 780±130 BP (16 950–16 520 cal BP). In the area of the present Szczerców field, the Parchliny-Chabielice site yielded three dates in the Krasówka River valley – 10 290±90 (12 450–11 830 cal BP), 11 840±110 BP (13 800–13 520 cal BP) and 15 440±160 (18 910–18 360 cal BP) (Tab. 1). Based on the collected dates, so-called PDF (Probability Density Functions) curves were drawn up in the range 20 000–11 000 cal BP (Fig. 3). The analysis used the shape analysis method of the summed probability density distributions of the calibrated radiocarbon dates (Fig. 3). The position of peaks of the obtained distributions on the time axis is in good agreement with the oxygen curve from the Greenland core NGRIP. There is a clear clustering of dates around the beginning of GI–1, but also around GI–1d, GI–1c2 and GI–1b. This was contributed to by the preferential sampling for dating from locations where changes in sediment lithology were evident (bottom and top of peat layer, soil on mineral deposits, bottom of oxbow fill, etc.). Therefore, the peaks of the obtained distributions should not be linked only to warm periods, but rather to transitions from cold to warm conditions or from warm to cold conditions. Although the dataset analysed is smallscale, the evident clustering of dates makes it possible to identify environmental responses to climate change over several time scales: - 19 500–18 900 cal BP (peak within GS–2b); - 17 800–17 300 cal BP (peak at transition GS––2b/GS–2a); - 16 800–16 600 cal BP (peak located at GS– –2a); - 15 200–14 300 cal BP (peak at GS–2a/GI–1e transition); - 14 100–13 800 cal BP (peak around GI–1d); - 13 600–13 550 cal BP (peak around GI–1c2); - 13 250–13 050 cal BP (peak around GI-1b); - 12 500–11 700 cal BP (peak around the second half of GS–1) The resolution of the analyses carried out does not make it possible to determine whether there were delays in the environmental response of the study area in relation to the changes recorded in the Greenlandic core, but any delays must be less than the average uncertainty of the radiocarbon dates analysed, i.e. ~100 years. The analyses carried out used dates made using conventional techniques, which have relatively large uncertainties. It is probable that including new sites and precise 14C age determinations by the AMS technique would contribute to a higher resolution of this type of analysis.

Stochastic transition behaviors in a tri-stable van der Pol oscillator with fractional delayed element subject to Gaussian white noise

The stochastic P-bifurcation behavior of tri stability in a generalized Van der Pol system with fractional derivative under additive Gaussian white noise excitation is investigated. Firstly, based on the minimal mean square error principle, the fractional derivative is found to be equivalent to a linear combination of damping and restoring forces, and the original system is simplified into an equivalent integer order system. Secondly, the stationary probability density function of the system amplitude is obtained by stochastic averaging, and according to the singularity theory, the critical parameters for stochastic P-bifurcation of the system are found. Finally, the nature of stationary probability density function curves of the system amplitude is qualitatively analyzed by choosing the corresponding parameters in each region divided by the transition set curves. The consistency between the analytical solutions and Monte-Carlo simulation results verifies the theoretical results in this paper.

Comparison between oil spill images and look-alikes: an evaluation of SAR-derived observations of the 2019 oil spill incident along Brazilian waters.

Three SAR-derived observations of dark surface patches along the Northeastern Brazilian coastline by the end of 2019 were misreported in the Brazilian media as oil spill-related. Unfortunately, these observations were misled by false positives or look-alikes. Therefore, this paper aims to technically evaluate these look-alike classes by analyzing image attributes found to be helpful to the identification of ocean targets, including oil spills, rain cells, biofilms, and low wind conditions. We use image augmentation to extend our dataset size and create the probability density function curves. The processing includes image segmentation, optimal attribute extraction, and classification with random forest classifiers. Our results contrast with the open-source oil spill detection system and patch classifier methodology called "RIOSS." Analysis of the feature probability density functions based on optimal attributes is promising since we could capture most of the false positive targets in the three SAR-reported images in 2019. The only exception was the biofilm slick observed on October 28th, where the RIOSS mistakenly classified this organic patch as a low wind region with oil spots. This pitfall is acceptable at this project stage since we had only five biogenic film samples to train the algorithm.

Transition behaviors of system energy in a bi-stable van Ver Pol oscillator with fractional derivative element driven by multiplicative Gaussian white noise

The stochastic P-bifurcation behavior of system energy in a bi-stable Van der Pol oscillator with fractional damping under multiplicative Gaussian white noise excitation is investigated. Firstly, using the principle of minimal mean square error, the non-linear stiffness terms can be equivalent to a linear stiffness which is a function of the system amplitude, and the original system is simplified to an equivalent integer order Van der Pol system. Secondly, the system amplitude?s stationary probability density function is obtained by stochastic averaging. Then, according to the singularity theory, the critical parametric conditions for the system amplitude?s stochastic P-bifurcation are found. Finally, the types of the system?s stationary probability density function curves of amplitude are qualitatively analyzed by choosing the corresponding parameters in each area divided by the transition set curves. The consistency between the analytical results and the numerical results obtained from Monte-Carlo simulation verifies the theoretical analysis in this paper, and the method used in this paper can directly guide the design of the fractional-order controller to adjust the response of the system.

Impacts of the Saharan air layer on the physical properties of the Atlantic tropical cyclone cloud systems: 2003–2019

Abstract. It is generally known that the tropical cyclone (TC) cloud systems (TCCSs) in the North Atlantic region frequently occur during boreal summer, while the Saharan dust outbreaks occur concurrently. The Sahara air layer (SAL), an elevated layer containing Saharan dry air and mineral dust, has crucial impacts on the generation and evolution of TCs. However, the effects of SAL on the physical (macro and micro) characteristics of the Atlantic TCCSs have not been well constrained, and the interaction mechanisms between them still need further investigation. In this study, our primary interest is to distinguish the various effects of SAL on different intensities of TCs and further find out the probable causes of the varied feedback mechanisms. Therefore, we attempt to identify whether and how the effects of the SAL play a positive or negative role on the TCCSs and to draw a qualitative conclusion on how SAL affects the various intensities of the TCs. This paper focuses on the 70 TC samples from July to September in the years of 2003–2019 to investigate the physical effects of SAL on three intensities of TCs, i.e., the tropical depression (TD), tropical storm (TS), and hurricane (HU). The results show that SAL has a positive impact on the macro properties of HU but significantly suppresses the TD. It appears that the SAL attributes little to the variation of the ice cloud effective radius (CERi) for TS, whereas CERi changes significantly and differentially for TD and HU. When affected by SAL, the probability density function (PDF) curve of CERi generally shifts to the smaller value for TD, whereas the PDF curve becomes flatter for HU. Our analysis indicates that the various responses of TCCSs to SAL are determined by the combined effects of dry air masses, the dust aerosols as ice nuclei, and the thermodynamic and moisture conditions. Based on the observation data analysis, a concept scheme description has been concluded to deepen our recognition of the effects of SAL on the TCCSs.

A new maximum entropy method for estimation of multimodal probability density function

The high-precision estimation of a multimodal probability density function is a difficult problem in many engineering fields. We propose a new method to improve the estimation accuracy based on the fractional moment-based maximum entropy method with a nonlinear transformation and a multi-peak recognition method. For the translation parameters in the nonlinear transformation, three approaches, such as sample-based least square polynomial fitting technique, sample-based kernel density estimation and classical maximum entropy method, are presented to determine the parameters. By adjusting the translation parameter, the valley of the probability density function curve can be translated to the position with a larger slope, and the distance between adjacent peaks is enlarged to avoid the wrong fitting form of the probability density function curve with multiple peaks. After the parameters of the transformation are obtained, the fractional moment-based maximum entropy method is applied to predict the probability density function of the transformed performance function. Two numerical examples are used to verify the accuracy and stability of the proposed method. Two engineering examples are introduced to illustrate the applicability and efficiency of the proposed method in the real-life engineering setting. It is concluded that the proposed method uses fewer moments with less additional calculation costs, and has good computational efficiency and applicability for the modeling of multimodal probability density functions, compared with the classical fractional moment-based maximum entropy method.

Quantitative comparison of measurement quality of cross-correlation based particle velocity instruments in different gas fluidization regimes

Particle velocity measurement based on cross-correlation algorithm is used widely in various multiphase flow systems. In this study, an optical fiber probe, i.e. Labasys® 100 from MSE Meili was used to systematically compare the qualities of its measured particle velocities in a pair of hydrodynamically distinct regimes, i.e. a fast fluidized bed (FFB) and a bubbling fluidized bed (BFB). Compared to FFBs, the measured particle velocities in BFBs show lower data repeatability, higher percentage of invalid data and higher sensitivity to the selection of the threshold value of maximum cross-correlation coefficient, which corresponds to much lower measurement qualities in this regime. Comparatively, higher measurement qualities were observed in the FFB based on the same evaluating methods. The highest measurement qualities appear in the core region of the riser and under high superficial gas velocities. A matchup relationship between the measurement quality and the shape of the probability density function (PDF) curves of maximum cross-correlation coefficient is found. Narrower PDF curves correspond to higher measurement quality and vice versa.

Adapting sudden landslide identification product (SLIP) and detecting real-time increased precipitation (DRIP) algorithms to map rainfall-triggered landslides in Western Cameroon highlands (Central-Africa)

BackgroundNASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This double method uses Landsat 8 satellite images and daily rainfall data for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands.MethodsThe SLIP algorithm is adapted, by integrating the inverse Normalized Difference Vegetation Index (NDVI) to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948–2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate landslide occurrence to rainfall, with the first known event in Cameroon as starting point, and using the Cox model.ResultsFrom the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time.ConclusionsThe proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime analysis for a more flexibility in observation and prediction thresholding.

Spatial variability of south Asian summer monsoon extreme rainfall events and their association with global climate indices

The study explores the spatial variability and related characteristics of south Asian summer monsoon rainfall and extreme rainfall events based on APHRODITE rainfall products. An attempt was also made to investigate their association with global climate indices. Most of the South Asian regions are vulnerable to extreme rainfall events and many of these lead to major hydrological disasters. Such events are adversely affecting flora and fauna, causing large-scale damage to the livelihood and economy. We have analysed the characteristics of extreme rainfall events during the summer monsoon season for 65 years (1951–2015). The statistical method based on the percentile of daily rainfall amount (>99th percentile) was used to identify the extreme rainfall zones. We have also attempted to explore the spatio-temporal features and heterogeneity of these extreme events at a finer spatial resolution. We examined the linear trend of these extreme rain events on the spatial domain and found that extreme rainfall decreases over northcentral Indian and southwestern coastal belts. An increase in the frequency and amount of rainfall from extreme rain events were observed in the western coastal belts between 16° to 20° N and central Indian regions along with the coastal areas of Bangladesh and Myanmar. The linear trends of extremes exhibit remarkable differences from the trends of seasonal rainfall. The results from the probability density function (PDF) analysis are also supporting the trend analysis of mean and extremes in all the five regions. It was also observed a distinct shift in the PDF curve during the pre and post 1980 periods. Moreover, we have analysed the relationship of various climate indices (Niño3.4, Modoki, DMI, PDO, AMO, and AZM) with the mean and extreme rainfalls in addition to the spatial trends. Most of the indices show a significant inverse relationship with mean and extreme rainfalls. The areas of significant correlation are less in the case of extreme rainfall while comparing with that of the mean rainfall.

State-of-health estimation of batteries in an energy storage system based on the actual operating parameters

The battery state-of-health (SOH) in a 20 kW/100 kW h energy storage system consisting of retired bus batteries is estimated based on charging voltage data in constant power operation processes. The operation mode of peak shaving and valley filling in the energy storage system is described in detail. Two SOH modeling methods including incremental capacity analysis (ICA) and probability density function (PDF) are compared. The results show that such battery SOH modeling methods as ICA and PDF are available under constant power conditions. The SOH estimation model by PDF method has an enhanced accuracy on the basis of the same voltage range. The height H of the largest peak in ICA or PDF curves has a linear positive correlation with the battery SOH, but the SOH model by ICA is more accurate than that by PDF. According to the SOH evaluation, the energy storage of the system will be significantly improved if some cells or modules with lower SOH are replaced by those with higher SOH. In the condition of unknown SOH of battery, the relative aging degree of battery can be estimated by grading the H value from ICA or PDF curves based on actual charging voltage data, which is helpful for the operation and maintenance of the energy storage system.