Stochastic Environmental Research and Risk Assessment

Spatial prediction of landslide susceptibility in Taleghan basin, Iran Abstract Identifying landslide-susceptible zones is warranted to prevent and mitigate associated hazards in mountainous regions, where a landslide is a destructive type of erosion. A landslide susceptibility map was developed for the Taleghan basin based on frequency ratio (FR), logistic regression (LR), maximum entropy (MaxEnt), and support vector machine (SVM) with radial base (RBF), sigmoid (SIG), linear (LN), and polynomial (PL) kernel functions. To this end, an inventory map with 166 landslide locations was prepared and partitioned into 70% and 30% to train and validate the models, respectively. Subsequently, the models were designed based on 13 factors including elevation, slope degree, slope aspect, distance to stream, Stream Power Index, Topographic Wetness Index, Stream Transport Index, distance to fault, lithology, soil texture, land use, distance to road and precipitation. The performance of the methods was assessed using the area under the receiver operating characteristic curve, the Seed Cell Area Index (SCAI), the precision index (P). Moreover, statistical measures including sensitivity, specificity, and accuracy were calculated. Friedman test was also applied to confirm significant statistical differences among the seven models employed in this research. The validation results showed that MaxEnt had the maximum area under the curve (0.812). The results obtained using the models LR, FR, PL-SVM, SIG-SVM, LN-SVM, and RBF-SVM were 0.807, 0.732, 0.679, 0.663, 0.643 and 0.660, respectively. The obtained P index showed the better performance of MaxEnt and LR models. Moreover, the trend of changes in the SCAI values, from low- to high-susceptibility, indicated that the MaxEnt and LR models had the best performance. Decision makers can effectively use the findings of the present study to mitigate the financial and human costs resulting from the landslides.

One-dimensional solute transport in open channel flow from a stochastic systematic perspective Abstract Solute transport by river and stream flows in natural environment has significant implication on water quality and the transport process is full of uncertainties. In this study, a stochastic one-dimensional solute transport model under uncertain open-channel flow conditions is developed. The proposed solute transport model is developed by upscaling the stochastic partial differential equations through their one-to-one correspondence to the nonlocal Lagrangian–Eulerian Fokker–Planck equations. The resulting Fokker–Planck equation is a linear and deterministic differential equation, and this equation can provide a comprehensive probabilistic description of the spatiotemporal evolutionary probability distribution of the underlying solute transport process by one single numerical realization, rather than requiring thousands of simulations in the Monte Carlo simulation. Consequently, the ensemble behavior of the solute transport process can also be obtained based on the probability distribution. To illustrate the capabilities of the proposed stochastic solute transport model, various steady and unsteady uncertain flow conditions are applied. The Monte Carlo simulation with stochastic Saint–Venant flow and solute transport model is used to provide the stochastic flow field for the solute transport process, and further to validate the numerical solute transport results provided by the derived Fokker–Planck equations. The comparison of the numerical results by the Monte Carlo simulation and the Fokker–Planck equation approach indicated that the proposed model can adequately characterize the ensemble behavior of the solute transport process under uncertain flow conditions via the evolutionary probability distribution in space and time of the transport process.

Compound effects of rainfall and storm tides on coastal flooding risk Abstract Rainfall and storm tides are both flood drivers in coastal zones. The complex interplay between them can lead to or exacerbate the impacts of flooding. While the dependence between rainfall and storm tides has been extensively studied, the compound effects of them on coastal flood risk have not been well researched. With Haikou city as the case study, this study investigates the bivariate return period of compounding rainfall and storm tide events based on copula functions and the failure probability is used to assess the variation of bivariate flood risk during the entire project lifetime. The results show that (1) there is a significant correlation between rainfall and storm tides. Therefore, bivariate RP analysis can provide more adequate and comprehensive information about risks than univariate RP analysis. Kendall RP can describe the bivariate RP more accurately since the dangerous region of Kendall RP is divided by the joint probability value. (2) Neglecting the compounding impacts and the dependence of rainfall and storm tides causes significant underestimation of the joint RP and failure probability. (3) The bivariate hydrologic risk value will decrease quickly when the design rainfall is higher than 100 mm and it becomes small and decrease slowly when the design rainfall exceeds 450 mm. Furthermore, the bivariate hydrologic risk value would not decrease until the storm tide is higher than 2 m and the values would become quite small as the storm tide exceeds 4.5 m. Such bivariate hydrologic risk analysis can provide decision support for hydraulic facility design as well as actual flood control and mitigation.

Toward parsimonious modeling of frequency of areal runoff from heavy-to-extreme precipitation in large urban areas under changing conditions: a derived moment approach Abstract Translating changes in land surface conditions and climate into changes in precipitation, runoff and flood frequencies over a range of catchment scales is a pressing challenge for hydrologic design and flood risk management today. In this paper, we describe a novel approach for modeling areal runoff frequency from heavy-to-extreme precipitation in large urban areas using a simple but general stochastic model for runoff at point scale and bi- and univariate parametric probability distributions for positive point precipitation and areal runoff, respectively. We apply the approach to the Dallas–Fort Worth area using a 22-year historical multisensor precipitation dataset from the National Weather Service to characterize how the different factors that specify the second-order statistics of precipitation, imperviousness and soil water holding capacity at point scale may shape areal runoff frequency, and to assess how changes in precipitation climatology and land surface conditions may change areal runoff frequency as a function of catchment scale and magnitude of precipitation. The results indicate that areal runoff frequency is impacted most significantly by changes in climatological mean and coefficient of variation of positive point precipitation, water holding capacity of soil, imperviousness, and spatial correlation scale of positive point precipitation given the probability of occurrence of heavy-to-extreme precipitation, and that a very small number of low-order statistics of point precipitation may describe areal runoff frequency given the conditional probability distribution models for point precipitation and areal runoff. The approach presented hence offers a parsimonious physically-based alternative to purely numerical approaches based on integrated modeling, or empirical approaches based solely on statistical modeling toward predictive modeling of areal precipitation and runoff frequencies under changing hydroclimatological conditions.

Hydrological post-processing based on approximate Bayesian computation (ABC) Abstract This study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be difficult to calculate the likelihood itself in hydrological modelling, specially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. The aim is to show that the conditional predictive distribution is qualitatively similar produced by the exact predictive (MCMC post-processor) or the approximate predictive (ABC post-processor). We also use MCMC post-processor as a benchmark to make results more comparable with the proposed method. We test the ABC post-processor in two scenarios: (1) the Aipe catchment with tropical climate and a spatially-lumped hydrological model (Colombia) and (2) the Oria catchment with oceanic climate and a spatially-distributed hydrological model (Spain). The main finding of the study is that the approximate (ABC post-processor) conditional predictive uncertainty is almost equivalent to the exact predictive (MCMC post-processor) in both scenarios.

Retraction Note to: Adaptive neuro-fuzzy evaluation of wind farm power production as function of wind speed and direction The Editor-in-Chief has retracted this article (Petković et al. 2015a) because validity of the content of this article cannot be verified.

Expression of Concern: Adaptation of ANFIS model to assess thermal comfort of an urban square in moderate and dry climate The Editor-in-Chief of Stochastic Environmental Research and Risk Assessment is issuing an editorial expression of concern for article.

Expression of Concern: Potential of adaptive neuro-fuzzy inference system for evaluation of drought indices The Editor-in-Chief of Stochastic Environmental Research and Risk Assessment is issuing an editorial expression of concern to alert readers that this article (Gocić et al. 2015) shows substantial indication of irregularities in authorship during the submission process.

Retraction Note to: Support vector regression methodology for prediction of output energy in rice production The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of peer review and authorship manipulation. The authors do not agree to this retraction.

Forecasting salinity time series using RF and ELM approaches coupled with decomposition techniques Abstract Using historical salinity data from nine drought periods in the Pearl River Delta of China, this study utilized two machine learning approaches to forecast the salinity time series for multistep lead times: random forest (RF) models and extreme learning machine (ELM) models. To improve conventional RF and ELM models, three signal decomposition techniques were applied to preprocess the input time series: empirical mode decomposition (EMD), wavelet decomposition (WD) and wavelet packet decomposition (WPD). The study results indicated that in contrast to conventional RF/ELM, a hybrid RF/ELM method accompanied by decomposition techniques displayed better forecasting performance and yielded reasonably accurate prediction results. More specifically, hybrid models coupled with WPD displayed the best performance for all three forecast lead times of one, three and five days, whereas EMD underperformed both WPD and WD because of the limited predictability of the components. Both the WPD and WD hybrid models using the \(coif5\) wavelet basis performed better than those using the other two bases (db8 and sym8). In addition, ELM method performed better for conventional and WD/WPD hybrid models, whereas the RF method worked better for EMD hybrid model. The findings of the study showed that the nonstationary salinity series could be transformed into several relatively stationary components in the decomposition process, which provided more accurate salinity forecasts. The developed hybrid models coupling RF/ELM method with decomposition techniques could be a feasible way for salinity prediction.

Alexandros Sfakianakis
Anapafseos 5 . Agios Nikolaos
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6948891480

alsfakia@gmail.com