A neural network approach to simple prediction of soil nitrification potential: A case study in Japanese temperate forests

A local model based on environmental variables clustering for estimating foliar phosphorus of rubber trees with vis-NIR spectroscopic data

Existing local models based on multiple environmental variables clustering (LM-MEVC) treat the influences of environmental factors on leaf phosphorus concentration (LPC) of rubber trees (Hevea brasiliensis) equally when grouping samples. In fact, the effects that environmental factors assert on LPC are different. So, environmental factors need to be treated differently so that the different effects can be taken into consideration when dividing samples into clusters or groups. According to this basic idea, a local model based on weighted environmental variables clustering (LM-WEVC) was developed. This approach consists of four steps. Firstly, the most important environmental variables that influence LPC were selected. Then, the weights of the selected environmental variables were determined. In the following, the selected environmental variables were weighted and used as clustering variables to group samples. Finally, within each cluster or group of samples, an estimation model was established. In order to verify its effectiveness in predicting LPC of rubber trees, the proposed method was applied to a case study in Hainan Island, China. Rubber tree (cultivar CATAS-7-33-97) leaf samples were collected from three different sampling periods. Spectral reflectance of the collected leaf samples was measured using an ASD spectroradiometer, FieldSpec 3. Leaf samples collected from the three different sampling periods were used separately to test LM-WEVC. Coefficient of determination (R²), root mean squared error (RMSE), and ratio of prediction deviation (RPD) were employed as evaluation criterion. Performance of LM-WEVC was compared with that of the existing LM-MEVC. Results indicated that for the three sampling periods, the prediction accuracies of LM-WEVC were always higher than those of LM-MEVC. The values of R² and RPD for LM-WEVC were increased by 8.15%–36.68%, and by 11.33%–59.40% respectively, while values of RMSE were reduced by 9.09%–37.5%, compared with those for LM-MEVC. These results demonstrate that LM-WEVC was effective in estimating LPC of rubber trees, and also confirmed our hypothesis that environmental factors unequally influenced LPC of rubber trees.

Spatial distribution prediction of soil As in a large-scale arsenic slag contaminated site based on an integrated model and multi-source environmental data

Different prediction models have important effects on the accuracy of spatial distribution simulations of heavy metals in soil. This study proposes a model (RFOK) combining a random forest (RF) with ordinary kriging (OK), multi-source environmental data such as terrain elements, site environmental elements, and remote sensing data were incorporated to predict the spatial distribution of heavy arsenic (As) in soil of a certain large arsenic slag site. The predictions results of RFOK were compared with those obtained using the RF, OK, inverse distance weighted (IDW), and stepwise regression (STEPREG) models for assessment of prediction accuracy. The results showed that arsenic pollution was widely distributed and the center of the site, including arsenic slag stacking area and production area were seriously polluted. The overall spatial distribution of arsenic pollution simulated by the five models was similar, but the IDW, RF, OK, and STEPREG showed less spatial variation of soil pollution, while RFOK simulation can better express the characteristics of details in change. The cross-validation results showed that RFOK had the lowest root-mean-square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) relative to the other four models, followed by RF, OK, IDW, and STEPREG. The RMSE, MAE and MRE of RFOK decreased by 62.2%, 64.3% and 68.7%, respectively, relative to the RF model with the second highest accuracy. Compared with the traditional spatial distribution prediction model, the RFOK model proposed in this study has excellent spatial distribution prediction ability for soil heavy metal pollution with large spatial variation characteristics, which can fully explain the nonlinear relationship between pollutant content and its environmental impact elements.

Modelling heavy metals build-up on urban road surfaces for effective stormwater reuse strategy implementation

Urban road stormwater is an alternative water resource to mitigate water shortage issues in the worldwide. Heavy metals deposited (build-up) on urban road surface can enter road stormwater runoff, undermining stormwater reuse safety. As heavy metal build-up loads perform high variabilities in terms of spatial distribution and is strongly influenced by surrounding land uses, it is essential to develop an approach to identify hot-spots where stormwater runoff could include high heavy metal concentrations and hence cannot be reused if it is not properly treated. This study developed a robust modelling approach to estimating heavy metal build-up loads on urban roads using land use fractions (representing percentages of land uses within a given area) by an artificial neural network (ANN) model technique. Based on the modelling results, a series of heavy metal load spatial distribution maps and a comprehensive ecological risk map were generated. These maps provided a visualization platform to identify priority areas where the stormwater can be safely reused. Additionally, these maps can be utilized as an urban land use planning tool in the context of effective stormwater reuse strategy implementation.