PMT: new analytical framework for automated evaluation of geo-environmental modelling approaches
Article
Article Title | PMT: new analytical framework for automated evaluation of geo-environmental modelling approaches |
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ERA Journal ID | 3551 |
Article Category | Article |
Authors | Rahmati, Omid (Author), Kornejady, Aiding (Author), Samadi, Mahmood (Author), Deo, Ravinesh C. (Author), Conoscenti, Christian (Author), Lombardo, Luigi (Author), Dayal, Kavina (Author), Taghizadeh-Mehrjardi, Ruhollah (Author), Pourghasemi, Hamid Reza (Author), Kumar, Sandeep (Author) and Bui, Dieu Tien (Author) |
Journal Title | Science of the Total Environment |
Journal Citation | 664, pp. 296-311 |
Number of Pages | 16 |
Year | 2019 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0048-9697 |
1879-1026 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.scitotenv.2019.02.017 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0048969719304966 |
Abstract | Geospatial computation, data transformation to a relevant statistical software, and step-wise quantitative performance assessment can be cumbersome, especially when considering that the entire modelling procedure is repeatedly interrupted by several input/output steps, and the self-consistency and self-adaptive response to the modelled data and the features therein are lost while handling the data from different kinds of working environments. To date, an automated and a comprehensive validation system,which includes both the cutoff-dependent and –independent evaluation criteria for spatial modelling approaches, has not yet been developed for GIS based methodologies. This study, for the first time, aims to fill this gap by designing and evaluating a user-friendly model validation approach, denoted as Performance Measure Tool (PMT), and developed using freely available Python programming platform. The considered cutoff-dependent criteria include receiver operating characteristic (ROC) curve, success-rate curve (SRC) and prediction-rate curve (PRC),whereas cutoff-independent consist of twenty-one performance metrics such as efficiency, misclassification rate, false omission rate, F-score, threat score, odds ratio, etc. To test the robustness of the developed tool, we applied it to a wide variety of geoenvironmental modelling approaches, especially in different countries, data, and spatial contexts around the world including, the USA (soil digital modelling), Australia (drought risk evaluation), Vietnam (landslide studies), Iran (flood studies), and Italy (gully erosion studies). The newly proposed PMT is demonstrated to be capable of analyzing a wide range of environmental modelling results, and provides inclusive performance evaluation metrics in a relatively short time and user-convenient framework whilst each of the metrics is used to address a particular aspect of the predictive model. Drawing on the inferences, a scenario-based protocol for model performance evaluation is suggested |
Keywords | PMT; spatial modelling; goodness-of-fit; validation; performance analysis; predictive model; evaluation framework |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
460207. Modelling and simulation | |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Ton Duc Thang University, Vietnam |
Islamic Azad University, Iran | |
University of Tehran, Iran | |
School of Agricultural, Computational and Environmental Sciences | |
University of Palermo, Italy | |
University of Twente, Netherlands | |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
Eberhard Karl University of Tübingen, Germany | |
Nanjing Normal University, China | |
South Dakota State University, United States | |
Duy Tan University, Vietnam | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q516q/pmt-new-analytical-framework-for-automated-evaluation-of-geo-environmental-modelling-approaches
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