Map on predicted deposition of Cs-137 in Spanish soils from geostatistical analyses

Caro, Alexis; Legarda, F.; Romero, L.; Herranz, M.; Barrera, M.; Valiño, F.; Idoeta, R.; Olondo, C.

doi:10.1016/j.jenvrad.2012.06.007

Cited by 22 publications

(7 citation statements)

References 17 publications

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“…The sill (C) is the summation of nugget and partial sill while the ratio of nugget (Cn) to sill (C) was employed to measure the degree of spatial structure (dependence) [23]. The variable: has a strong spatial dependence, if the ratio is less than 25%; has moderate spatial dependence, if the ratio is between 25% and 75%; and shows only weak spatial dependence, if the ratio is greater than 75% [24].…”

Section: Resultsmentioning

confidence: 99%

Emission dispersion modeling and geospatial analysis of vehicular emissions in some parts of Benin City, Nigeria

Ilaboya

Otuaro²

2021

J. Adv. Sci. Eng.

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Over the years, decline in air quality has been connected to the growing rate of urbanization and increasing number of vehicles on the roads. Most of the pollutants emitted from vehicular activities have been observed to have adverse effects on individuals as well as the atmosphere. Although, the focus of this study is to develop an emission dispersion model to predict the concentration of specific air pollutants with distance, the application of geostatistical technique such as Kriging interpolation to study the spatial distribution of pollutants from vehicular emissions around the study area was also exemplified. Seven (7) georeferenced points, namely, Ugbowo main gate, Ekosodin junction, Agen junction, Super D junction, Nitel junction, Okhunmwun junction and Oluku market junction were used for data collection. Pollutants from vehicular emissions, namely, nitrogen dioxide (NO2), carbon monoxide (CO) including the total radiation were monitored in the morning and evening for a period of 35 days (7th July to 12th August 2020) with the aid of portable toxic gas monitors and radiation alert meters. Other parameters of interest, which were also measured include maximum temperature and wind speed using infra-red thermometers and portable anemometer respectively. To ascertain the quality of the data, selected preliminary analysis, namely, test of normality, test of homogeneity, outlier detection and reliability test were done. Result of the study showed a high concentration of NO2, CO and total radiation around Ugbowo main gate and Okhunmwun community and environs especially during the peak hours of evening (5.0 p.m. – 6.0 p.m.) when the traffic load is high.

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Section: Resultsmentioning

confidence: 99%

Emission dispersion modeling and geospatial analysis of vehicular emissions in some parts of Benin City, Nigeria

Ilaboya

Otuaro²

2021

J. Adv. Sci. Eng.

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“…(Cambardella et al, 1994). This criterion has been used to evaluate the spatial dependence of groundwater EC (Arslan, 2012) and soil 137 Cs element (Caro et al, 2013). According to Tab.…”

Section: Resultsmentioning

confidence: 99%

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

et al. 2015

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Artificial intelligence (AI) systems have opened a new horizon to analyze water engineering and environmental problems in recent decades. In this study performances of ordinary kriging (OK) as a linear geostatistical estimator and two intelligent methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are investigated. For this purpose, geographical coordinates of 120 observation wells that located in Tabriz plain, north-west of Iran, were defined as inputs and groundwater electrical conductivities (EC) were set as output of models. Eighty percent of data were randomly selected to train and develop mentioned models and twenty percent of data used for testing and validating. Finally, the outputs of models were compared with the corresponding measured values in observation wells. Results indicated that ANFIS model provided the best accuracy among models with the root mean squared error (RMSE) value of 1.69 dS.m -1 and correlation coefficient (R) of 0.84. The RMSE values in ANN and OK were calculated 1.97 and 2.14 dS.m -1 and the R values were determined 0.79 and 0.76, respectively. According to the results, the ANFIS method predicted EC precisely and can be advised for modeling groundwater salinity.

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“…The minimum and maximum ranges indicates the highest and the lowest spatial variability (Jeihouni et al 2018), belonged to hardness and EC, respectively. Moreover, the factors nugget to sill ratio (%) was evaluated as a criterion for classifying the spatial dependence (Caro et al 2013). The ratio less than 25% indicates the strong spatial dependence, the ratio between 25 and 75% indicates the moderate spatial dependence, and the ratio greater than 75% indicates the weak spatial dependence of the variable (Cambardella et al 1994).…”

Section: Methodsmentioning

confidence: 99%

Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

Jeihouni

Toomanian

Mansourian

2019

Water Resour Manage

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Groundwater is an important source to supply drinking water demands in both arid and semiarid regions. Nevertheless, locating high quality drinking water is a major challenge in such areas. Against this background, this study proceeds to utilize and compare five decision treebased data mining algorithms including Ordinary Decision Tree (ODT), Random Forest (RF), Random Tree (RT), Chi-square Automatic Interaction Detector (CHAID), and Iterative Dichotomiser 3 (ID3) for rule induction in order to identify high quality groundwater zones for drinking purposes. The proposed methodology works by initially extracting key relevant variables affecting water quality (electrical conductivity, pH, hardness and chloride) out of a total of eight existing parameters, and using them as inputs for the rule induction process. The algorithms were evaluated with reference to both continuous and discrete datasets. The findings were speculative of the superiority, performance-wise, of rule induction using the continuous dataset as opposed to the discrete dataset. Based on validation results, in continuous dataset, RF and ODT showed higher and RT showed acceptable performance. The groundwater quality maps were generated by combining the effective parameters distribution maps using inducted rules from RF, ODT, and RT, in GIS environment. A quick glance at the generated maps reveals a drop in the quality of groundwater from south to north as well as from east to west in the study area. The RF showed the highest performance (accuracy of 97.10%) among its counterparts; and so the generated map based on rules inducted from RF is more reliable. The RF and ODT methods are more suitable in the case of continuous dataset and can be applied for rule induction to determine water quality with higher accuracy compared to other tested algorithms.

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Map on predicted deposition of Cs-137 in Spanish soils from geostatistical analyses

Cited by 22 publications

References 17 publications

Emission dispersion modeling and geospatial analysis of vehicular emissions in some parts of Benin City, Nigeria

Emission dispersion modeling and geospatial analysis of vehicular emissions in some parts of Benin City, Nigeria

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS

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