PMT: new analytical framework for automated evaluation of geo-environmental modelling approaches

Rahmati, Omid and Kornejady, Aiding and Samadi, Mahmood and Deo, Ravinesh C. and Conoscenti, Christian and Lombardo, Luigi and Dayal, Kavina and Taghizadeh-Mehrjardi, Ruhollah and Pourghasemi, Hamid Reza and Kumar, Sandeep and Bui, Dieu Tien (2019) PMT: new analytical framework for automated evaluation of geo-environmental modelling approaches. Science of the Total Environment, 664. pp. 296-311. ISSN 0048-9697

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


Statistics for USQ ePrint 35758
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 14 Mar 2019 23:59
Last Modified: 15 Mar 2019 04:33
Uncontrolled Keywords: PMT; spatial modelling; goodness-of-fit; validation; performance analysis; predictive model; evaluation framework
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
Identification Number or DOI: 10.1016/j.scitotenv.2019.02.017
URI: http://eprints.usq.edu.au/id/eprint/35758

Actions (login required)

View Item Archive Repository Staff Only