The main objective of the present study was to compare the performance of a classifier that implements the Logistic Regression and a classifier that employs a Naïve Bayes algorithm in landslide ...susceptibility assessments. The study provides an evaluation concerning the influence of model's complexity and the size of the training data, while it identifies the most accurate and reliable classifier.
The comparison of the two classifiers was based on the assessment of a database containing 116 sites located at the mountains of Epirus, Greece, where serious landslides events have been encountered. The sites are classified into two categories, non-landslide and landslide areas. The identification of those areas was established by analysing airborne imagery, extensive field investigation and the examination of previous research studies. The geo-environmental conditions in those locations where analyzed in regard with their susceptibility to slide. In particular, seven variables where analyzed: engineering geological units, slope angle, slope aspect, mean annual rainfall, distance from river network, distance from tectonic features and distance from road network.
Multicollinearity analysis and feature selection was implemented in order to estimate the conditional independence among the variables and to rank the variables according to their significance in estimating landslide susceptibility. By the above processes the construction of nine different datasets was accomplished. Further partition allowed creating subsets of training and validating data from the original 116 sites. Each dataset was characterized by the number of the variables used and the size of the training datasets.
The comparison and validation of the outcomes of each model was achieved using statistical evaluation measures, the receiving operating characteristic and the area under the success and predictive rate curves. The results indicated that model's complexity and the size of the training dataset influence the accuracy and the predictive power of the models concerning landslide susceptibility. In particular, the most accurate model with high predictive power was the eighth model (five variables and 92 training data), with the Naïve Bayes classifier having a slightly higher overall performance and accuracy than the Logistic Regression classifier, 87.50% and 82.61% on the validation datasets, respectively. The highest area under the curve was achieved by the Naïve Bayes classifier for both the training and validating datasets (0.875 and 0.806 respectively) while the Logistic Regression classifier achieved a lower AUC values for the training and validating datasets (0.844 and 0.711, respectively). When limited data are available it seems that more accurate and reliable results could be obtained by generative classifiers, like Naïve Bayes classifiers. Overall, landslide susceptibility assessments could serve as a useful tool for the local and national authorities, in order to evaluate strategies to prevent and mitigate the adverse impacts of landslide events.
•Logistic regression and Naïve Bayes were used in landslide susceptibility zoning.•Model complexity and the size of training data influence the prediction accuracy.•The reduction in model's complexity improved the generalization performance.•The Naïve Bayes model outperforms the Logistic regression.
Regarding the ever increasing issue of water scarcity in different countries, the current study plans to apply support vector machine (SVM), random forest (RF), and genetic algorithm optimized random ...forest (RFGA) methods to assess groundwater potential by spring locations. To this end, 14 effective variables including DEM-derived, river-based, fault-based, land use, and lithology factors were provided. Of 842 spring locations found, 70% (589) were implemented for model training, and the rest of them were used to evaluate the models. The mentioned models were run and groundwater potential maps (GPMs) were produced. At last, receiver operating characteristics (ROC) curve was plotted to evaluate the efficiency of the methods. The results of the current study denoted that RFGA, and RF methods had better efficacy than different kernels of SVM model. Area under curve (AUC) of ROC value for RF and RFGA was estimated as 84.6, and 85.6%, respectively. AUC of ROC was computed as SVM- linear (78.6%), SVM-polynomial (76.8%), SVM-sigmoid (77.1%), and SVM- radial based function (77%). Furthermore, the results represented higher importance of altitude, TWI, and slope angle in groundwater assessment. The methodology created in the current study could be transferred to other places with water scarcity issues for groundwater potential assessment and management.
Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the ...advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide "obviously" interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that: 1) clinicians and practitioners can subsequently approach these methods with caution; 2) insight into interpretability will be born with more considerations for medical practices; and 3) initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged.
Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to ...find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Västerås, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.
•Stochastic and dynamic EV charging demand is modelled based on human travel pattern.•Heterogeneous temporal and spatial EV charging demands is distributed in urban area.•A cell-based geographic partition method is proposed for charging station planning.•Impacts of service range, economic parameters and charging strategies are considered.
The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that ...can support decision-making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are among the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision-making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. The geographical criteria are geology, land use, sediment, erosion, slope, groundwater, and discharge. The water quality criteria include Soluble Sodium Percentage, Total Dissolved Solid, Potential of Hydrogen, and Electrical Conductivity of water. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this study area. Actual locations of dams constructed in the area were used to verify the results of both methods.
The process of selecting the optimum route is complicated due to the enormous number of variables that must be considered in order to attain the best results. This research seeks to determine the ...best route via combining a set of assessment criteria into a structured framework based on Geographic Information System (GIS) using the analytical hierarchy process (AHP). In this study, a two-stage model was used in which a multi-criteria assessment and GIS are used together in the process of determining highway alignment. In the first stage of the model, the design stage and path alternatives are created from different perspectives. While in the second stage, which is the selection stage, where the alignment alternatives were created in the first stage are compared and the best ones are selected. The proposed model was tested in the study area located between the cities of Ramadi and Hit. The results showed that the resulted alignment is clearly applicable and many criteria can be taken into account in the process. Moreover, it is noted that the proposed model can generate different alignment alternatives with different viewpoints. Finally, it was concluded that the use of GIS contributes to the application of the model quite easily.
Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the ...main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.
•DeLone and McLean success models have not been applied consistently.•This meta-review examined 53 prior studies published between 1992 and 2019.•Dimensionality and interdependence of success ...dimensions may need attention.•Scope of reciprocal relationships in success models could need reconsideration.•Nomological net for examining information systems success could be crucial.
Considerable research has focused on information system success (ISS) over the years largely using the models proposed by DeLone and McLean (DM) in 1992 and 2003. Several relationships found in the DM models have been sporadically supported in empirical research although the complete DM models have not been consistently applied. Studies have also interchanged relationships in the 1992 and 2003 models, tested relationships between ISS dimensions unspecified in the DM models, and examined relationships between ISS dimensions and other factors. This study presents a critical meta-review of 53 studies using DM models published between 1992 and 2019, identifies the state of ISS research, and raises several directions for research.
QGIS is a user friendly, open source geographic information system (GIS). The popularity of open source GIS and QGIS, in particular, has been growing rapidly over the last few years. This book is ...designed to help beginners learn about all the tools required to use QGIS 3.4.
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