排序方式： 共有37条查询结果，搜索用时 15 毫秒
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Rahim?BarzegarEmail author Asghar?Asghari Moghaddam Jan?Adamowski Elham?Fijani 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(10):2705-2718
Groundwater is an especially important freshwater source for water supplies in the Maku area of northwest Iran. The groundwater of the area contains high concentrations of fluoride and is, therefore, important in predicting the fluoride contamination of the groundwater for the purpose of planning and management. The present study aims to evaluate the ability of the extreme learning machine (ELM) model to predict the level of fluoride contamination in the groundwater in comparison to multilayer perceptron (MLP) and support vector machine (SVM) models. For this purpose, 143 water samples were collected in a five-year period, 2004–2008. The samples were measured and analyzed for electrical conductivity, pH, major chemical ions and fluoride. To develop the models, the data set—including Na+, K+, Ca2+ and HCO3 ? concentrations as the inputs and fluoride concentration as the output—was divided into two subsets; training/validation (80% of data) and testing (20% of data), based on a cross-validation technique. The radial basis-based ELM model resulted in an R 2 of 0.921, an NSC of 0.9071, an RMSE of 0.5638 (mg/L) and an MABE of 0.4635 (mg/L) for the testing data. The results showed that the ELM models performed better than MLP and SVM models for prediction of fluoride contamination. It was observed that ELM models learned faster than the other models during model development trials and the SVM models had the highest computation time. 相似文献
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Hamid Salahshoor Alexey Lyubushin Elham Shabani Javad Kazemian 《Journal of Seismology》2018,22(6):1515-1527
Bayesian probability theory is an appropriate and useful method for estimating parameters in seismic hazard analysis. The analysis in Bayesian approaches is based on a posterior belief, also their special ability is to take into account the uncertainty of parameters in probabilistic relations and a priori knowledge. In this study, we benefited the Bayesian approach in order to estimate maximum values of peak ground acceleration (Amax) also quantiles of the relevant probabilistic distributions are figured out in a desired future interval time in Iran. The main assumptions are Poissonian character of the seismic events flow and properties of the Gutenberg-Richter distribution law. The map of maximum possible values of Amax and also map of 90% quantile of distribution of maximum values of Amax on a future interval time 100 years is presented. According to the results, the maximum value of the Amax is estimated for Bandar Abbas as 0.3g and the minimum one is attributed to Esfahan as 0.03g. Finally, the estimated values in Bayesian approach are compared with what was presented applying probabilistic seismic hazard (PSH) methods based on the conventional Cornel (1968) method. The distribution function of Amax for future time intervals of 100 and 475 years are calculated for confidence limit of probability level of 90%. 相似文献
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Rajesh Chittor Sundaram Elham Naghizade Renata Borovica‐Gajic Martin Tomko 《Transactions in GIS》2020,24(4):1001-1032
Missing data in Volunteered Geographic Information (VGI) are an unavoidable consequence of data collection by non‐experts, guided by only vague and informal mapping guidelines. While various Missing Value Imputation (MVI) techniques have been proposed as data cleansing strategies, they have primarily targeted numerical data attributes in non‐spatial databases. There remains a significant gap in methods for imputing nominal attribute values (e.g., Street Name) in map databases. Here, we present an imputation algorithm called the Membership Imputation Algorithm (MIA), targeting spatial databases and enabling imputation of nominal values in spatially referenced records. By targeting membership classes of spatial objects, MIA harnesses spatio‐temporal characteristics of data and proposes efficient heuristics to impute the class name (i.e., a membership). Experimental results show that the proposed algorithm is able to impute the membership with high levels of accuracy (over 94%) when assigning Street Name(s), across highly diverse regional contexts. MIA is effective in challenging spatial contexts such as street intersections. Our research serves as a first step in highlighting the effectiveness of spatio‐temporal measures as a key driver for nominal imputation techniques. 相似文献
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Karim Nassar Wael Elham Mahmod Hassan Fath Ali Masria Kazuo Nadaoka Abdelazim Negm 《Marine Georesources & Geotechnology》2019,37(1):81-95
This study focuses on the shoreline change detection along the North Sinai coast in Egypt using geographic information system and digital shoreline analysis system (DSAS) during the elapsed period from 1989 to 2016. The measurement of shoreline variation is mainly described for three zones: zone I, El-Tinah plain bay; zone II, El-Bardawil Lake; zone III, El-Arish valley. The rates of shoreline changes in the form of erosion and accretion patterns are automatically quantified by four statistical parameters functioned in DSAS namely endpoint rate, net shoreline movement, linear regression rate (LRR), and least median of squares. LRR results elucidate that the western seaside of El-Tinah plain bay has experienced an extremely dynamic feature with an average erosion rate of ?8.17?m/year. The littoral drifts have been driven by eastward alongshore currents toward the east side of the bay to be accreted with an average rate of +4.28?m/year. Moreover, the shoreline has progressed west of El-Bardawil inlet (1), El-Bardawil inlet (2), and El-Arish harbor. Subsequently, the corresponding average beach growth rates are found to be +2.7, +8.5, and +6.5?m/year, respectively. In contrast, the shoreline on the down-drift side to the east has negatively retreated, and the corresponding beaches have regressed at rates of ?4.5, ?8.65, and ?2.9?m/year, respectively. 相似文献
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Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches 总被引：2，自引：0，他引：2
Bahram Choubin Gholamreza Zehtabian Ali Azareh Elham Rafiei-Sardooi Farzaneh Sajedi-Hosseini Özgür Kişi 《Environmental Earth Sciences》2018,77(8):314
Interest in semiarid climate forecasting has prominently grown due to risks associated with above average levels of precipitation amount. Longer-lead forecasts in semiarid watersheds are difficult to make due to short-term extremes and data scarcity. The current research is a new application of classification and regression trees (CART) model, which is rule-based algorithm, for prediction of the precipitation over a highly complex semiarid climate system using climate signals. We also aimed to compare the accuracy of the CART model with two most commonly applied models including time series modeling (ARIMA), and adaptive neuro-fuzzy inference system (ANFIS) for prediction of the precipitation. Various combinations of large-scale climate signals were considered as inputs. The results indicated that the CART model had a better results (with Nash–Sutcliffe efficiency, NSE?>?0.75) compared to the ANFIS and ARIMA in forecasting precipitation. Also, the results demonstrated that the ANFIS method can predict the precipitation values more accurately than the time series model based on various performance criteria. Further, fall forecasts ranked “very good” for the CART method, while the ANFIS and the time series model approximately indicated “satisfactory” and “unsatisfactory” performances for all stations, respectively. The forecasts from the CART approach can be helpful and critical for decision makers when precipitation forecast heralds a prolonged drought or flash flood. 相似文献