Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models

Quantitative analyses of groundwater flow and transport typically rely on a physically‐based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data‐driven models (DDMs) to reduce the predictive error of physically‐based groundwater models. Two machine learning techniques, the instance‐based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real‐world case studies of the Republican River Compact Administration model and the Spokane Valley‐Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root‐mean‐square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically‐based model.     

Probabilistic data integration for characterization of spatial distribution of residual LNAPL

The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF). The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration, namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies.     

The use of geomorphological elements for evaluation of visual quality of Israeli coast

This study describes a method for quantitative evaluation of visual resources of the Mediterranean coast of Israel. The method used visual characteristics of contrast, variety, competition, composition and harmony to identify visually preferred sites. Various coastal physical elements were used as a measurement scale of the characteristics. The study also examined the use of photographic slides vs. filed visit as a tool for visual resources evaluation of the Israeli coast. Findings were based on data gathered from a questionnaire given to two groups in the laboratory and in the field. Statistical analysis of the findings indicated that the proposed method could be used effectively to measure visual preference of the Israeli coast using physical elements and quantitative values. The data also show that the use of photographic slides in laboratory conditions is as effective a tool as field observation for the evaluation of observer's visual preference of the coast.     

Agrobiodiversity of Muda Rice Agroecosystem:A case Study in Largest Granary Area of Malaysia

A survey was carried out at the largest rice cultivation area in Peninsular Malaysia,the Muda rice agroecosystem.The main objective of this study was to document the overall biodiversity associated with this unique agroecosystem by using a combination of sampling techniques in order to record different groups of fauna and flora.The total number of biota recorded and identified from the rice field ecosystem during the study period consisted of 46 species of zooplankton,81 species of aquatic insects,5 species of rodents,7 species of bats,87 species of birds,11 species of fishes and 58 species of weeds.A long-term study should be carried out as more species are expected to be recorded when more of the Muda rice agroecosystem area has been sampled to obtain sufficient information on the Muda rice agrobiodiversity.     

Empirical modeling of plate load test moduli of soil via gene expression programming

New empirical models were developed to predict the soil deformation moduli using gene expression programming (GEP). The principal soil deformation parameters formulated were secant (E_s) and reloading (E_r) moduli. The proposed models relate E_s and E_r obtained from plate load-settlement curves to the basic soil physical properties. The best GEP models were selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon a series of plate load tests conducted on different soil types at depths of 1–24 m. To verify the applicability of the derived models, they were employed to estimate the soil moduli of a part of test results that were not included in the analysis. The external validation of the models was further verified using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting E_s and E_r. The proposed models give precise estimates of the soil deformation moduli. The E_s prediction model provides considerably better results in comparison with the model developed for E_r. The simplified formulation for E_s significantly outperforms the empirical equations found in the literature. The derived models can reliably be employed for pre-design purposes.     

The planets capture model of V838 Monocerotis: conclusions for the penetration depth of the planet(s)

V838 Mon is the prototype of a new class of objects. Understanding the nature of its multistage outburst and similar systems is challenging. So far, several scenarios have been invoked to explain this group of stars. In this work, the planets-swallowing model for V838 Mon is further investigated, taking into account the findings that the progenitor is most likely a massive B-type star. We find that the super-Eddington luminosity during the eruption can explain the fast rising times of the three peaks in the optical light curve. We used two different methods to estimate the location where the planets were consumed. There is a nice agreement between the values obtained from the luminosities of the peaks and from their rising time-scale. We estimate that the planets were stopped at a typical distance of one solar radius from the centre of the host giant star. The planets-devouring model seems to give a satisfying explanation to the differences in the luminosities and rising times of the three peaks in the optical light curve of V838 Mon. The peaks may be explained by the consumption of three planets or alternatively by three steps in the terminal falling process of a single planet. We argue that only the binary merger and the planets-swallowing models are consistent with the observations of the new type of stars defined by V838 Mon.     

A hybrid computational approach to formulate soil deformation moduli obtained from PLT

In this study, new empirical equations were developed to predict the soil deformation moduli utilizing a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed models relate secant (E_s), unloading (E_u) and reloading (E_r) moduli obtained from plate load–settlement curves to the basic soil physical properties. Several models with different combinations of the influencing parameters were developed and checked to select the best GP/SA models. The database used for developing the models was established upon a series of plate load tests (PLT) conducted on different soil types at various depths. The validity of the models was tested using parts of the test results that were not included in the analysis. The validation of the models was further verified using several statistical criteria. A traditional GP analysis was performed to benchmark the GP/SA models. The contributions of the parameters affecting E_s, E_u and E_r were analyzed through a sensitivity analysis. The proposed models are able to estimate the soil deformation moduli with an acceptable degree of accuracy. The E_s prediction model has a remarkably better performance than the models developed for predicting E_u and E_r. The simplified formulations for E_s, E_u and E_r provide significantly better results than the GP-based models and empirical models found in the literature.     

Geochemistry and zircon U-Pb geochronology of Aligoodarz granitoid complex, Sanandaj-Sirjan Zone, Iran

The Aligoodarz granitoid complex (AGC) is located in the Sanandaj-Sirjan Zone (SSZ), western Iran and consists of quartz-diorites, granodiorites and subordinate granites. Whole rock major and trace element data mostly define linear trends on Harker diagrams suggesting a cogenetic origin of the different rock types. (⁸⁷Sr/⁸⁶Sr)_i and εNd_t ratios are in the ranges 0.7074-0.7110 and −3.56 to −5.50, respectively. The trace elements and Sr-Nd isotopic composition suggest that the granitoids from the AGC are similar to crustal derived I-type granitoids of continental arcs. The whole rock suite was produced by assimilation and fractional crystallization starting from a melt with intermediate composition likely possessing a mantle component. In situ zircon U-Pb data on the granites with LA-ICP-MS yield a crystallization age of ∼165 Ma. Inherited grains spanning in age from ∼180 Ma up to 2027 Ma were also found and confirm that assimilation of country rock has occurred.Chemical and chronological data on the AGC were compared with those available for other granitoid complexes of the central SSZ (e.g., Dehno, Boroujerd and Alvand). The comparison reveals that in spite of the different origins that have been proposed, all these granitoid complexes are likely genetically related. They share many chemical features and are derived from crustal melts with minor differences. Alvand granites have the most peculiar compositions most likely related to the presence of abundant pelitic component. All these intrusions are coeval and reveal the presence of an extensive magmatic activity in the central sector of the SSZ during middle Jurassic.     

APG:A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.