关键词： habitat characteristics   potential habitat modeling   artificial neural networks   support vector machines   decision tree CHAID algorithm  

摘要： Ferula ovina (Boiss.) is a valuable but vulnerable monocarpic perennial forb species from Apiaceae plant family whose habitat has been degraded over the years;hence identifying the factors controlling its distribution is important to assist range managers for the reclamation activities. The specific objective of this study was to investigate the quantitative relationships between soil properties, topographical features, climate factors and F. ovina distribution in a semiarid part of central Iran and to assess the relative importance of these factors in controlling its spatial variability. To discern these complex relationships, artificial neural networks (ANNs), support vector machines (SVMs), and decision tree CHAID algorithm were employed. Results from the ANN, SVM, and CHAID models indicated that the climate and topographic conditions should be considered more in explaining the variability in F. ovina occurrence and distribution. Factors such as slope, precipitation of warmest month, and minimum temperature of coldest month were identified by the ANN, SVM, and CHAID models as the determinant factors influencing the spatial distribution of F. ovina in central Iran, respectively. Furthermore, CHAID approach showed greater potential in predicting the F. ovina occurrence in the study area. This study provides a strong basis for identifying the most determinant habitat characteristics of F. ovina and other vulnerable or endangered plant species in semiarid rangelands of Iran;however, its general analytical framework could be applied to other parts of the world with similar challenges.

关键词： Auto loan   Dominance-based Rough Set Balanced Rule   Ensemble   Decision rules   Random Forest   SVM  

摘要： Financial fraud is escalating as financial services and operations grow. Despite preventive actions and security measures deployed to mitigate financial fraud, fraudsters are learning and finding new ways to get around fraud prevention systems, thereby, challenging quantitative techniques and predictive models. Thus, new techniques must be explored and tested so the insights obtained from the analysis may be used to support more accurate fraud prediction and the development of fraud prevention systems which have additional checks to mitigate suspicious events. Auto loan is a significant financial product not yet explored in the literature, unlike the misuse of credit cards, for instance. Given the recent increase in fraudulent transactions concerning auto loan applications, this paper tests a new data set for auto loan applications using a technique not yet explored for financial fraud prediction, namely the Dominance-based Rough Set Balanced Rule Ensemble (DRSA-BRE), and after comparing it with other techniques traditionally used for predicting financial fraud, finds that the proposed approach has several advantages over the traditional ones. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Machine learning   Organic solid waste   Modeling   Prediction  

摘要： Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.

关键词： leaf physiognomy   machine learning   paleobotany   paleoclimate   precipitation   temperature  

摘要： Digital leaf physiognomy (DLP) is considered as one of the most promising methods for estimating past climate. However, current models built using the DLP data set still lack precision, especially for mean annual precipitation (MAP). To improve predictive power, we developed five machine learning (ML) models for mean annual temperature (MAT) and MAP respectively, and then tested the precision of these models and some of their averaging compared with that obtained from other models. The precision of all models was assessed using a repeated stratified 10-fold cross-validation. For MAT, three combinations of models (R-2 = .77) presented moderate improvements in precision over the multiple linear regression (MLR) model (R-2 = .68). For log(e)(MAP), the averaging of the support vector machine (SVM) and boosting models improved the R-2 from .19 to .63 compared with that of the MLR model. For MAP, the R-2 of this model combination was 0.49, which was much better than that of the artificial neural network (ANN) model (R-2 = .21). Even the bagging model, which had the lowest R-2 (.37) for log(e)(MAP), demonstrated better precision (R-2 = .27) for MAP. Our palaeoclimate estimates for nine fossil floras were also more accurate, because they were in better agreement with independent paleoclimate evidence. Our study confirms that our ML models and their averaging can improve paleoclimatic reconstructions, providing a better understanding of the relationship between climate and leaf physiognomy.

关键词： Early mature rice   Machine learning (ML) methods   Multiple linear regression (MLR)   Rice yield prediction   Phenology  

摘要： Rice (Oryza sativa L.) is a staple cereal crop and its demand is substantially increasing with the growth of the global population. Precisely predicting rice yields are of vital importance to ensure the food security in countries like China, where rice accounts for one-fifth of the total agricultural production. Previous studies found that the rice yields had been significantly impacted by climate change. In addition, phenological variables were found to be important factors concerning rice yields due to its fundamental role in carbon allocation between plant organs, but its impacts on rice yields were seldom evaluated. In this study, eleven combinations of phenology, climate and geography data were tested to predict the site-based rice yields using a traditional regression-based method (MLR, multiple linear regression), and more advanced three machine learning (ML) methods: back-propagation neural network (BP), support vector machine (SVM) and random forest (RF). The results showed that ML methods were more precise than MLR method. The combination using the integrated phenology, climate during growing season and geographical information was better for yields predictions than other combinations across the ML methods, e.g. the difference RMSE (R-2) between prediction and observed rice yields were 800 (0.24), 737 (0.33), and 744 (0.31) kg/ha for BP, SVM and RF, respectively. The SVM had achieved the highest precisions in yield predictions and the phenological variables substantially improved the accuracy of yield predictions, and the relative importance of phenological variables were even similar as climatic variables. We highlight the phenology and climate need to be accurately represented in the crop models to improve the accuracy in rice yield prediction under climate change conditions using integrated ML methods.

关键词： soil moisture   downscaling   SMAP   MODIS   machine learning  

摘要： Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors;(2) adding geographical factors can significantly improve the performance of SM downscaling;(3) integrating LST, LEE, and geographical factors got the best performance;(4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.

关键词： Kernel logistic regression   Kernel functions   Hybrid models   Landslide   Geographic information system  

摘要： Globally, but especially in China, landslides are considered to be one of the most severe and significant natural hazards. In this study, bivariate statistical-based kernel logistic regression (KLR) models with different kernel functions (Polynomial, PUK, and Radial Basis Function), named the PLKLR, PUKLR, and RBFKLR models, were proposed for landslide susceptibility evaluation in Zichang City, China. Meanwhile, the present study aims to build landslide susceptibility maps based on bivariate statistical correlation analysis, optimization of different kernel functions, comparison of three landslide susceptibility maps and systematic analysis of spatial patterns. The steps of this article are organized as follows: Firstly, a landslide inventory containing 263 historical landslide locations was constructed. For the purpose of training and validation of models, 263 landslide locations were randomly divided into two parts with a ratio of 70/30. Secondly, 14 landslide conditioning factors were extracted from the spatial database. Subsequently, correlation analysis between the conditioning factors and the occurrence of landslides was conducted using frequency ratios. Then, the conditioning factors with normalized frequency ratios values were used as inputs to build the landslide susceptibility maps using the three models. A multicollinearity analysis was performed using collinearity statistics. Finally, the area under the receiver operating characteristic curve (AUC) was used for comparison and validation of models for recognizing the prediction capability. By further quantitative comparing mapped susceptibility values on a pixel-by-pixel basis, which can acquire underestimations and overestimations of factors (distance to river and slope) and susceptibility area. The results indicated that the PUKLR model had superior performance in landslide susceptibility assessment, with the highest AUC values of 0.884 and 0.766 for training and validation datasets, respectively. This

关键词： vegetation mapping   geo-objects   remote sensing   multi-source geospatial data   sample collection and purification  

摘要： Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids' labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area;thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to

关键词： Machine learning   Whale optimization algorithm   Wavelet transform   Flood frequency   Climate change   Fuzzy uncertainty  

摘要： In the present study, for the first time, a new framework is used by combining metaheuristic algorithms, decomposition and machine learning for flood frequency analysis under climate-change conditions and application of HadCM3 (A2 and B2 scenarios), CGCM3 (A2 and A1B scenarios) and CanESM2 (RCP2.6, RCP4.5 and RCP8.5 scenarios) in global climate models (GCM). In the proposed framework, Multivariate Adaptive Regression Splines (MARS) and M5 Model tree are used for classification of precipitation (wet and dry days), whale optimization algorithm (WOA) is considered for training least square support vector machine (LSSVM), wavelet transform (WT) is used for decomposition of precipitation and temperature, LSSVM-WOA, LSSVM, K nearest neighbor (KNN) and artificial neural network (ANN) are performed for downscaling precipitation and temperature, and discharge is simulated under present period (1972-2000), near future (2020-2040) and far future (2070-2100). Log normal distribution is used for flood frequency analysis. Furthermore, analysis of variance (ANOVA) and fuzzy method are employed for uncertainty analysis. Karun3 Basin, in southwest of Iran, is considered as a case study. Results indicated that MARS performed better than M5 model tree. In downscaling, ANN and LSSVM_WOA slightly outperformed other machine learning algorithms. Results of simulating the discharge showed superiority of LSSVM_WOA_WT algorithm (Nash-Sutcliffe efficiency (NSE) = 0.911). Results of flood frequency analysis revealed that 200-year discharge decreases for all scenarios, except CanESM2 RCP2.6 scenario, in the near future. In the near and far future periods, it is obvious from ANOVA uncertainty analysis that hydrological models are one of the most important sources of uncertainty. Based on the fuzzy uncertainty analysis, HadCM3 model has lower uncertainty in higher return periods (up to 60% lower than other models in 1000-year return period).

关键词： Fracture toughness   Knitting fabric   Pure and hybrid composite   Finite element analysis   Machine learning algorithms  

摘要： In recent years, it became possible to use different methods for the analysis of mechanical systems with the help of computers to learn like humans and by increasing their interaction with the world by observing autonomously. One of these mechanical analyzes is the fracture mechanics in which the behavior of the laminated composites having a crack is examined. In this study, experimental methods, finite element analysis (FEA) and machine learning algorithms (MLA) were used to analyze the fracture behavior of polymer composites in Mode I, Mode I/II and Mode II loading situations. For the experimental study, the fracture behaviors of the laminated composites reinforced with pure glass, pure carbon and glass/carbon hybrid knitted fabrics were tested with the help of Arcan test apparatus. In the finite element method, the linear elastic fracture behavior at the crack tip was analyzed by using the J-integral method. In the field of MLA, there is no single learning algorithm that provides good learning on all real-world problem data. Therefore, algorithm selection is done experimentally so various machine algorithms were used in the study. The analysis result showed that the finite element analysis and machine learning results were in good agreement with experimental measurements. This study is particularly important for the comparison of machine learning techniques with FEA in regression applications.