{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:04:41Z","timestamp":1780542281407,"version":"3.54.1"},"reference-count":143,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100010713","name":"Suncor Energy Incorporated","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010713","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Modelling & Software"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.envsoft.2026.107007","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:07:37Z","timestamp":1778170057000},"page":"107007","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["On the complementary roles of XVARS and SHAP in explainable AI"],"prefix":"10.1016","volume":"203","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3922-7363","authenticated-orcid":false,"given":"Banamali","family":"Panigrahi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saman","family":"Razavi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorne E.","family":"Doig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoshin","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karsten","family":"Liber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.envsoft.2026.107007_bib1","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.envsoft.2026.107007_bib2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1139\/S07-038","article-title":"Process water treatment in Canada's oil sands industry: I. Target pollutants and treatment objectives","volume":"7","author":"Allen","year":"2008","journal-title":"J. Environ. Eng. Sci."},{"issue":"11","key":"10.1016\/j.envsoft.2026.107007_bib3","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.1007\/s11269-021-02911-6","article-title":"Pipe break rate assessment while considering physical and operational factors: a methodology based on global positioning system and data-driven techniques","volume":"35","author":"Amiri-Ardakani","year":"2021","journal-title":"Water Resour. Manag."},{"issue":"2","key":"10.1016\/j.envsoft.2026.107007_bib4","doi-asserted-by":"crossref","DOI":"10.1111\/risa.12117","article-title":"Uncertainty in climate change modeling: can global sensitivity analysis be of help?","volume":"34","author":"Anderson","year":"2014","journal-title":"Risk Anal."},{"issue":"6","key":"10.1016\/j.envsoft.2026.107007_bib5","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1016\/j.watres.2011.12.007","article-title":"Effects of exposure to oil sands process-affected water from experimental reclamation ponds on Chironomus dilutus","volume":"46","author":"Anderson","year":"2012","journal-title":"Water Res."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107007_bib6","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1021\/es202415g","article-title":"Effectiveness of ozonation treatment in eliminating toxicity of oil sands process-affected water to Chironomus dilutus","volume":"46","author":"Anderson","year":"2012","journal-title":"Environ. Sci. Technol."},{"issue":"2","key":"10.1016\/j.envsoft.2026.107007_bib7","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s00477-020-01776-2","article-title":"Short-term water quality variable prediction using a hybrid CNN\u2013LSTM deep learning model","volume":"34","author":"Barzegar","year":"2020","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"10.1016\/j.envsoft.2026.107007_bib8","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.scitotenv.2019.03.107","article-title":"The toxicity of organic fractions from aged oil sands process-affected water to aquatic species","volume":"669","author":"Bauer","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envsoft.2026.107007_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2020.107189","article-title":"Meta functions for benchmarking in sensitivity analysis","volume":"204","author":"Becker","year":"2020","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.envsoft.2026.107007_bib10","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","author":"Bent\u00e9jac","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.envsoft.2026.107007_bib11","doi-asserted-by":"crossref","first-page":"7933","DOI":"10.1002\/2017WR020767","article-title":"Making the most out of a hydrological model data set: sensitivity analyses to open the model black-box","volume":"53","author":"Borgonovo","year":"2017","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib12","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.ejor.2015.06.032","article-title":"Sensitivity analysis: a review of recent advances","volume":"248","author":"Borgonovo","year":"2016","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib13","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.1021\/acs.est.7b05381","article-title":"Predicting daily urban fine particulate matter concentrations using a Random Forest model","volume":"52","author":"Brokamp","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envsoft.2026.107007_bib14","first-page":"1509","article-title":"An effective screening design for sensitivity analysis of large models. Environ. Modelling & software, modelling, comput. Assisted simul. Map","volume":"22","author":"Campolongo","year":"2007","journal-title":"Dangerous Phenomena for Hazard Assess"},{"key":"10.1016\/j.envsoft.2026.107007_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120212","article-title":"Fuzzy inference system with interpretable fuzzy rules: advancing explainable artificial intelligence for disease diagnosis\u2014A comprehensive review","volume":"662","author":"Cao","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.envsoft.2026.107007_bib16","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s00704-011-0510-0","article-title":"Combining a weather generator and a standard sensitivity analysis method to quantify the relevance of weather variables on agrometeorological models outputs","volume":"108","author":"Confalonieri","year":"2012","journal-title":"Theor. Appl. Climatol."},{"issue":"16","key":"10.1016\/j.envsoft.2026.107007_bib17","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1016\/j.ecolmodel.2010.04.021","article-title":"Comparison of sensitivity analysis techniques: a case study with the rice model WARM","volume":"221","author":"Confalonieri","year":"2010","journal-title":"Ecol. Model."},{"issue":"6498","key":"10.1016\/j.envsoft.2026.107007_bib18","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1126\/science.aba9647","article-title":"\u201cExplaining\u201d machine learning reveals policy challenges","volume":"368","author":"Coyle","year":"2020","journal-title":"science"},{"key":"10.1016\/j.envsoft.2026.107007_bib19","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1002\/etc.5341","article-title":"Delineating effluent exposure and cumulative ecotoxicological risk of metals downstream of a Saskatchewan uranium mill using autonomous sensors","volume":"41","author":"Cupe\u2010Flores","year":"2022","journal-title":"Environ. Toxicol. Chem."},{"key":"10.1016\/j.envsoft.2026.107007_bib20","series-title":"2016 IEEE Symposium on Security and Privacy (SP)","first-page":"598","article-title":"Algorithmic transparency via quantitative input influence: theory and experiments with learning systems","author":"Datta","year":"2016"},{"issue":"7","key":"10.1016\/j.envsoft.2026.107007_bib21","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1007\/s10661-024-12761-5","article-title":"Selenium bioaccumulation in Daphnia pulex via aqueous and dietary exposure","volume":"196","author":"Davila\u2010Arenas","year":"2024","journal-title":"Environ. Monit. Assess."},{"key":"10.1016\/j.envsoft.2026.107007_bib22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00244-024-01071-z","article-title":"Toxicity evaluation of water and pore water from a pilot\u2010scale pit lake in the Alberta oil sands region to Daphnia species","volume":"87","author":"Davila\u2010Arenas","year":"2024","journal-title":"Arch. Environ. Contam. Toxicol."},{"key":"10.1016\/j.envsoft.2026.107007_bib23","doi-asserted-by":"crossref","DOI":"10.1029\/2019WR025436","article-title":"Correlation effects? A major but often neglected component in sensitivity and uncertainty analysis","volume":"56","author":"Do","year":"2020","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2016.03.002","article-title":"Initial geochemical characteristics of fluid fine tailings in an oil sands end pit lake, Sci. Total Environ","volume":"556","author":"Dompierre","year":"2016","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.envsoft.2026.107007_bib25","article-title":"Evaluation of CFD and machine learning methods on predicting greenhouse microclimate parameters with the assessment of seasonality impact on machine learning performance","volume":"19","author":"El Alaoui","year":"2023","journal-title":"Sci. Afr."},{"key":"10.1016\/j.envsoft.2026.107007_bib26","series-title":"Better Regulation Toolbox","year":"2015"},{"key":"10.1016\/j.envsoft.2026.107007_bib27","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jhydrol.2016.03.026","article-title":"An overview of current applications, challenges, and future trends in distributed process\u2010based models in hydrology","volume":"537","author":"Fatichi","year":"2016","journal-title":"J. Hydrol."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107007_bib28","first-page":"1","article-title":"Multivariate adaptive regression splines","volume":"19","author":"Friedman","year":"1991","journal-title":"Ann. Stat."},{"key":"10.1016\/j.envsoft.2026.107007_bib29","series-title":"Game Theory","author":"Fudenberg","year":"1991"},{"key":"10.1016\/j.envsoft.2026.107007_bib30","article-title":"Are machine learning interpretations reliable? A stability study on global interpretations","author":"Gan","year":"2025","journal-title":"arXiv preprint arXiv:2505.15728"},{"key":"10.1016\/j.envsoft.2026.107007_bib31","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1016\/j.scitotenv.2019.02.093","article-title":"Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion","volume":"664","author":"Garosi","year":"2019","journal-title":"Sci. Total Environ."},{"issue":"5","key":"10.1016\/j.envsoft.2026.107007_bib32","doi-asserted-by":"crossref","first-page":"623","DOI":"10.2747\/1548-1603.49.5.623","article-title":"An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA","volume":"49","author":"Ghimire","year":"2012","journal-title":"GIScience Remote Sens."},{"key":"10.1016\/j.envsoft.2026.107007_bib33","article-title":"Towards automatic concept-based explanations","volume":"32","author":"Ghorbani","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.envsoft.2026.107007_bib34","article-title":"Explainable AI: current status and future directions","author":"Gohel","year":"2021","journal-title":"arXiv preprint arXiv:2107.07045"},{"key":"10.1016\/j.envsoft.2026.107007_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2020.07.007","article-title":"A practical tutorial on bagging and boosting based ensembles for machine learning: algorithms, software tools, performance study, practical perspectives and opportunities","volume":"64","author":"Gonz\u00e1lez","year":"2020","journal-title":"Inf. Fusion"},{"issue":"6","key":"10.1016\/j.envsoft.2026.107007_bib37","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1109\/MIS.2019.2957223","article-title":"Factual and counterfactual explanations for black box decision making","volume":"34","author":"Guidotti","year":"2019","journal-title":"IEEE Intell. Syst."},{"key":"10.1016\/j.envsoft.2026.107007_bib38","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.envsoft.2019.07.007","article-title":"Introductory overview of identifiability analysis: a guide to evaluating whether you have the right type of data for your modeling purpose","volume":"119","author":"Guillaume","year":"2019","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107007_bib39","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.comnet.2019.01.026","article-title":"An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring","volume":"151","author":"Guo","year":"2019","journal-title":"Comput. Netw."},{"key":"10.1016\/j.envsoft.2026.107007_bib40","series-title":"Sensitivity Analysis in Earth Observation Modelling","first-page":"397","article-title":"Chapter 20 - challenges and future outlook of sensitivity analysis","author":"Gupta","year":"2017"},{"key":"10.1016\/j.envsoft.2026.107007_bib41","doi-asserted-by":"crossref","first-page":"8692","DOI":"10.1029\/2018WR022668","article-title":"Revisiting the basis of sensitivity analysis for dynamical earth system models","volume":"54","author":"Gupta","year":"2018","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib42","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.envsoft.2017.03.031","article-title":"Insights into sensitivity analysis of Earth and environmental systems models: on the impact of parameter perturbation scale","volume":"95","author":"Haghnegahdar","year":"2017","journal-title":"Environ. Model. Software"},{"issue":"2","key":"10.1016\/j.envsoft.2026.107007_bib43","doi-asserted-by":"crossref","DOI":"10.1097\/00004032-199502000-00005","article-title":"A comparison of sensitivity analysis techniques","volume":"68","author":"Hamby","year":"1995","journal-title":"Health Phys."},{"key":"10.1016\/j.envsoft.2026.107007_bib44","series-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","first-page":"5540","article-title":"Evaluating explainable AI: which algorithmic explanations help users predict model behavior?","author":"Hase","year":"2020"},{"issue":"2","key":"10.1016\/j.envsoft.2026.107007_bib45","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s44230-023-00017-3","article-title":"A comparative study, prediction and development of chronic kidney disease using machine learning on patients clinical records","volume":"3","author":"Hassan","year":"2023","journal-title":"Human-Centric Intelligent Systems"},{"key":"10.1016\/j.envsoft.2026.107007_bib46","series-title":"Springer Series in Statistics","article-title":"The elements of statistical learning: data mining, inference, and prediction","author":"Hastie","year":"2009"},{"issue":"7","key":"10.1016\/j.envsoft.2026.107007_bib47","doi-asserted-by":"crossref","DOI":"10.5194\/hess-17-2893-2013","article-title":"Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models","volume":"17","author":"Herman","year":"2013","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.envsoft.2026.107007_bib48","doi-asserted-by":"crossref","first-page":"7023","DOI":"10.1002\/2015WR017175","article-title":"Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories","volume":"51","author":"Hipsey","year":"2015","journal-title":"Water Resour. Res."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107007_bib49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0951-8320(96)00002-6","article-title":"Importance measures in global sensitivity analysis of nonlinear models","volume":"52","author":"Homma","year":"1996","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.envsoft.2026.107007_bib50","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2018.00588","article-title":"Applying a global sensitivity analysis workflow to improve the computational efficiencies in physiologically-based pharmacokinetic modeling","volume":"9","author":"Hsieh","year":"2018","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.envsoft.2026.107007_bib51","series-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","first-page":"4198","article-title":"Towards faithfully interpretable NLP systems: how should we define and evaluate faithfulness?","author":"Jacovi","year":"2020"},{"key":"10.1016\/j.envsoft.2026.107007_bib52","article-title":"Feature relevance quantification in explainable AI: a causal problem","volume":"2020","author":"Janzing","year":"2020"},{"key":"10.1016\/j.envsoft.2026.107007_bib53","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s11269-019-02447-w","article-title":"Decision tree-based data mining and rule induction for identifying high quality groundwater zones to water supply management: a novel hybrid use of data mining and GIS","volume":"34","author":"Jeihouni","year":"2020","journal-title":"Water Resour. Manag."},{"key":"10.1016\/j.envsoft.2026.107007_bib54","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1162\/neco.1997.9.7.1493","article-title":"Dimension reduction by local principal component analysis","volume":"9","author":"Kambhatla","year":"1997","journal-title":"Neural Comput."},{"issue":"1\/2","key":"10.1016\/j.envsoft.2026.107007_bib55","doi-asserted-by":"crossref","first-page":"81","DOI":"10.2307\/2332226","article-title":"A new measure of rank correlation","volume":"30","author":"Kendall","year":"1938","journal-title":"Biometrika"},{"issue":"9","key":"10.1016\/j.envsoft.2026.107007_bib56","doi-asserted-by":"crossref","first-page":"12875","DOI":"10.1007\/s11356-021-13875-w","article-title":"Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation","volume":"29","author":"Khullar","year":"2021","journal-title":"Environ. Sci. Pollut. Control Ser."},{"key":"10.1016\/j.envsoft.2026.107007_bib57","doi-asserted-by":"crossref","first-page":"301","DOI":"10.9798\/KOSHAM.2020.20.6.301","article-title":"Analysis of major rainfall factors affecting inundation based on observed rainfall and Random Forest","volume":"20","author":"Kim","year":"2020","journal-title":"Korean Soc. Hazard Mitig."},{"key":"10.1016\/j.envsoft.2026.107007_bib58","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1002\/sdr.4260110403","article-title":"Sensitivity analysis and optimization of system dynamics models: regression analysis and statistical design of experiments","volume":"11","author":"Kleijnen","year":"1995","journal-title":"Syst. Dyn. Rev."},{"key":"10.1016\/j.envsoft.2026.107007_bib59","series-title":"Handbook of Uncertainty Quantification","first-page":"1","article-title":"Derivative-based global sensitivity measures","author":"Kucherenko","year":"2016"},{"key":"10.1016\/j.envsoft.2026.107007_bib60","series-title":"Monte Carlo and Quasi-Monte Carlo Methods","first-page":"455","article-title":"Derivative-based global sensitivity measures and their link with Sobol\u2019 sensitivity indices","author":"Kucherenko","year":"2016"},{"key":"10.1016\/j.envsoft.2026.107007_bib61","series-title":"Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society","first-page":"131","article-title":"Faithful and customizable explanations of black box models","author":"Lakkaraju","year":"2019"},{"key":"10.1016\/j.envsoft.2026.107007_bib62","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.matcom.2013.02.002","article-title":"Derivative-based global sensitivity measures: general links with Sobol\u2019 indices and numerical tests","volume":"87","author":"Lamboni","year":"2013","journal-title":"Math. Comput. Simulat."},{"issue":"8","key":"10.1016\/j.envsoft.2026.107007_bib63","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.3390\/s18082674","article-title":"Machine learning in agriculture: a review","volume":"18","author":"Liakos","year":"2018","journal-title":"Sensors"},{"key":"10.1016\/j.envsoft.2026.107007_bib64","article-title":"Technical guide-smart water extreme","year":"2020","journal-title":"Document version: v7.3- 02\/2020"},{"issue":"1","key":"10.1016\/j.envsoft.2026.107007_bib65","article-title":"\u2018One size does not fit all\u2019: a roadmap of purpose-driven mixed-method pathways for sensitivity analysis of agent-based models","volume":"23","author":"Ligmann-Zielinska","year":"2020","journal-title":"J. Artif. Soc. Soc. Simulat."},{"key":"10.1016\/j.envsoft.2026.107007_bib66","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1145\/3236386.3241340","article-title":"The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery","volume":"16","author":"Lipton","year":"2017","journal-title":"ACM Queue"},{"issue":"45","key":"10.1016\/j.envsoft.2026.107007_bib67","article-title":"Human attention-guided explainable AI for object detection","volume":"45","author":"Liu","year":"2023","journal-title":"Proceedings of the Annual Meeting of the Cognitive Science Society"},{"key":"10.1016\/j.envsoft.2026.107007_bib68","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.envsoft.2026.107007_bib69","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"56\u201367","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"issue":"10\u201311","key":"10.1016\/j.envsoft.2026.107007_bib70","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1016\/j.ress.2005.11.015","article-title":"Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model prediction","volume":"91","author":"Makowski","year":"2006","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"7","key":"10.1016\/j.envsoft.2026.107007_bib71","first-page":"37","article-title":"Ensemble feature selection and classification of internet traffic using XGBoost classifier","volume":"11","author":"Manju","year":"2019","journal-title":"Int. J. Comput. Netw. Inf. Secur."},{"key":"10.1016\/j.envsoft.2026.107007_bib72","series-title":"Crop Physiology","first-page":"349","article-title":"Model-assisted phenotyping and ideotype design","author":"Martre","year":"2015"},{"issue":"3","key":"10.1016\/j.envsoft.2026.107007_bib73","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1093\/aob\/mcy080","article-title":"A new methodology based on sensitivity analysis to simplify the recalibration of functional\u2013structural plant models in new conditions","volume":"122","author":"Mathieu","year":"2018","journal-title":"Annals of botany"},{"key":"10.1016\/j.envsoft.2026.107007_bib74","article-title":"A machine learning method based on stacking heterogeneous ensemble learning for prediction of indoor humidity of greenhouse","volume":"16","author":"Melal","year":"2024","journal-title":"J. Agric. Food Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib75","series-title":"Pitfalls to Avoid when Interpreting Machine Learning Models","author":"Molnar","year":"2020"},{"key":"10.1016\/j.envsoft.2026.107007_bib76","series-title":"Interpretable Machine Learning","author":"Molnar","year":"2022"},{"key":"10.1016\/j.envsoft.2026.107007_bib77","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/00401706.1991.10484804","article-title":"Factorial sampling plans for preliminary computational experiments","volume":"33","author":"Morris","year":"1991","journal-title":"Technometrics"},{"key":"10.1016\/j.envsoft.2026.107007_bib78","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.chemosphere.2017.01.072","article-title":"A risk-based approach for identifying constituents of concern in oil sands process-affected water from the Athabasca Oil Sands region","volume":"173","author":"McQueen","year":"2017","journal-title":"Chemosphere"},{"key":"10.1016\/j.envsoft.2026.107007_bib79","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s12273-015-0245-4","article-title":"A performance comparison of sensitivity analysis methods for building energy models","volume":"8","author":"Nguyen","year":"2015","journal-title":"Build. Simulat."},{"key":"10.1016\/j.envsoft.2026.107007_bib80","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1214\/aos\/1176325504","article-title":"Lattice sampling revisited: monte carlo variance of means over randomized orthogonal arrays","volume":"22","author":"Owen","year":"1994","journal-title":"Ann. Statist."},{"key":"10.1016\/j.envsoft.2026.107007_bib81","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolmodel.2021.109648","article-title":"Sensitivity analysis using Morris: just screening or an effective ranking method?","volume":"455","author":"Paleari","year":"2021","journal-title":"Ecol. Model."},{"key":"10.1016\/j.envsoft.2026.107007_bib82","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemosphere.2024.143679","article-title":"Spatio-temporal analysis of water chemistry and ecotoxicological risk characterisation for a constructed pilot-scale pit lake in the Athabasca oil sands region, Canada","volume":"368","author":"Panigrahi","year":"2024","journal-title":"Chemosphere"},{"key":"10.1016\/j.envsoft.2026.107007_bib83","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2025.121142","article-title":"Water quality assessment of a novel pilot-scale Pit Lake in the Alberta oil sands Region","volume":"272","author":"Panigrahi","year":"2025","journal-title":"Environ. Res."},{"issue":"3","key":"10.1016\/j.envsoft.2026.107007_bib84","doi-asserted-by":"crossref","DOI":"10.1029\/2024WR037398","article-title":"On robustness of the explanatory power of machine learning models: insights from a new explainable AI approach using sensitivity Analysis","volume":"61","author":"Panigrahi","year":"2025","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib85","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.125441","article-title":"Worth of hydraulic and water chemistry observation data in terms of the reliability of surface water-groundwater exchange flux predictions under varied flow conditions","volume":"590","author":"Partington","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.107007_bib86","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"the Journal of machine Learning research"},{"key":"10.1016\/j.envsoft.2026.107007_bib87","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1002\/ieam.4644","article-title":"Application of autonomous sensor technology to estimate selenium exposure and a site\u2010specific selenium threshold in a Canadian boreal lake","volume":"19","author":"Peixoto Mendes","year":"2023","journal-title":"Integrated Environ. Assess. Manag."},{"key":"10.1016\/j.envsoft.2026.107007_bib88","article-title":"Rise: randomized input sampling for explanation of black-box models","author":"Petsiuk","year":"2018","journal-title":"arXiv preprint arXiv:1806.07421"},{"key":"10.1016\/j.envsoft.2026.107007_bib89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2015.01.004","article-title":"A simple and efficient method for global sensitivity analysis based on cumulative distribution functions","volume":"67","author":"Pianosi","year":"2015","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107007_bib90","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.envsoft.2016.02.008","article-title":"Sensitivity analysis of environmental models: a systematic review with practical workflow","volume":"79","author":"Pianosi","year":"2016","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107007_bib91","doi-asserted-by":"crossref","DOI":"10.1007\/s41324-020-00346-6","article-title":"Crop yield prediction in cotton for regional level using random forest approach","volume":"29","author":"Prasad","year":"2021","journal-title":"spatial information research"},{"issue":"53","key":"10.1016\/j.envsoft.2026.107007_bib92","first-page":"1","article-title":"Tunability: importance of hyperparameters of machine learning algorithms","volume":"20","author":"Probst","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib93","article-title":"The battle of total-order sensitivity","author":"Puy","year":"2020","journal-title":"Estimators arXiv:2009.01147 [stat]"},{"key":"10.1016\/j.envsoft.2026.107007_bib94","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.geomorph.2017.09.006","article-title":"Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion","volume":"298","author":"Rahmati","year":"2017","journal-title":"Geomorphology"},{"key":"10.1016\/j.envsoft.2026.107007_bib95","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1002\/2013WR014063","article-title":"Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models: distributed evaluation of local sensitivity analysis","volume":"50","author":"Rakovec","year":"2014","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib96","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1002\/2015WR017558","article-title":"A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory","volume":"52","author":"Razavi","year":"2016","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib97","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1002\/2015WR017559","article-title":"A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application","volume":"52","author":"Razavi","year":"2016","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib98","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2021.105159","article-title":"Deep learning, explained: fundamentals, explainability, and bridgeability to process-based modelling","volume":"144","author":"Razavi","year":"2021","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107007_bib99","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.envsoft.2018.10.005","article-title":"VARS-TOOL: a toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis","volume":"112","author":"Razavi","year":"2019","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107007_bib100","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2020.104954","article-title":"The future of sensitivity analysis: an essential discipline for systems modeling and policy support","volume":"137","author":"Razavi","year":"2021","journal-title":"Environ. Modell. Software"},{"key":"10.1016\/j.envsoft.2026.107007_bib101","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.1109\/TNN.2011.2163169","article-title":"A new formulation for feedforward neural networks","volume":"22","author":"Razavi","year":"2011","journal-title":"IEEE Trans. Neural Network."},{"issue":"3","key":"10.1016\/j.envsoft.2026.107007_bib102","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1093\/sysbio\/45.3.380","article-title":"The probabilistic basis of jaccard's index of similarity","volume":"45","author":"Real","year":"1996","journal-title":"Syst. Biol."},{"key":"10.1016\/j.envsoft.2026.107007_bib103","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1135","article-title":"Why should i trust you? Explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"issue":"2018","key":"10.1016\/j.envsoft.2026.107007_bib104","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.apgeochem.2018.03.013","article-title":"The interplay of methane and ammonia as key oxygen consuming constituents in early stage development of Base Mine Lake, the first demonstration oil sands pit lake","volume":"93","author":"Risacher","year":"2018","journal-title":"Appl. Geochem."},{"key":"10.1016\/j.envsoft.2026.107007_bib105","series-title":"Explainable Machine Learning for Scientific Insights and Discoveries 10.1109\/ACCESS.2020","author":"Roscher","year":"2020"},{"key":"10.1016\/j.envsoft.2026.107007_bib106","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"issue":"2","key":"10.1016\/j.envsoft.2026.107007_bib107","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s11269-019-02463-w","article-title":"Receiving more accurate predictions for longitudinal dispersion coefficients in water pipelines: training group method of data handling using extreme learning machine conceptions","volume":"34","author":"Saberi-Movahed","year":"2020","journal-title":"Water Resour. Manag."},{"issue":"6","key":"10.1016\/j.envsoft.2026.107007_bib108","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1007\/s42979-023-02246-6","article-title":"Multi-step ahead urban water demand forecasting using deep learning models","volume":"4","author":"Sahoo","year":"2023","journal-title":"SN Comput. Sci."},{"issue":"5","key":"10.1016\/j.envsoft.2026.107007_bib109","doi-asserted-by":"crossref","DOI":"10.1061\/JIDEDH.IRENG-10441","article-title":"Comparative evaluation of machine-learning models for predicting daily evapotranspiration in a naturally ventilated greenhouse","volume":"151","author":"Sahoo","year":"2025","journal-title":"J. Irrigat. Drain. Eng."},{"key":"10.1016\/j.envsoft.2026.107007_bib110","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/S0010-4655(02)00280-1","article-title":"Making best use of model evaluations to compute sensitivity indices","volume":"145","author":"Saltelli","year":"2002","journal-title":"Comput. Phys. Commun."},{"key":"10.1016\/j.envsoft.2026.107007_bib111","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-019-11865-8","article-title":"A short comment on statistical versus mathematical modelling","author":"Saltelli","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.envsoft.2026.107007_bib112","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2021.105226","article-title":"Sensitivity analysis: a discipline coming of age","volume":"146","author":"Saltelli","year":"2021","journal-title":"Environ. Model. Software"},{"issue":"1","key":"10.1016\/j.envsoft.2026.107007_bib113","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/00401706.1999.10485594","article-title":"A quantitative model independent method for global sensitivity analysis of model output","volume":"41","author":"Saltelli","year":"1999","journal-title":"Technometrics"},{"key":"10.1016\/j.envsoft.2026.107007_bib114","first-page":"377","article-title":"Sensitivity anaysis as an ingredient of modeling","volume":"15","author":"Saltelli","year":"2000","journal-title":"Stat. Sci."},{"issue":"3","key":"10.1016\/j.envsoft.2026.107007_bib115","doi-asserted-by":"crossref","DOI":"10.1016\/0951-8320(95)00099-2","article-title":"About the use of rank transformation in sensitivity analysis of model output","volume":"50","author":"Saltelli","year":"1995","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.envsoft.2026.107007_bib116","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s11053-018-9416-1","article-title":"Self-learning random forests model for mapping groundwater yield in data-scarce areas","volume":"28","author":"Sameen","year":"2019","journal-title":"Natural Resources Research"},{"key":"10.1016\/j.envsoft.2026.107007_bib117","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/978-3-030-28954-6_1","article-title":"Towards explainable artificial intelligence","author":"Samek","year":"2019","journal-title":"Lect. Notes Comput. Sci."},{"key":"10.1016\/j.envsoft.2026.107007_bib118","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.envsoft.2016.02.005","article-title":"Global sensitivity analysis of environmental models: convergence and validation","volume":"79","author":"Sarrazin","year":"2016","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107007_bib119","article-title":"Position paper: bridging the gap between machine learning and sensitivity analysis","author":"Scholbeck","year":"2023","journal-title":"arXiv preprint arXiv:2312.13234"},{"key":"10.1016\/j.envsoft.2026.107007_bib120","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.envsoft.2018.09.002","article-title":"Global sensitivity analysis for high-dimensional problems: how to objectively group factors and measure robustness and convergence while reducing computational cost","volume":"111","author":"Sheikholeslami","year":"2019","journal-title":"Environ. Model. Software"},{"issue":"11","key":"10.1016\/j.envsoft.2026.107007_bib121","doi-asserted-by":"crossref","DOI":"10.1061\/(ASCE)HE.1943-5584.0001574","article-title":"Improved understanding of river ice processes using global sensitivity analysis approaches","volume":"22","author":"Sheikholeslami","year":"2017","journal-title":"J. Hydrol. Eng."},{"key":"10.1016\/j.envsoft.2026.107007_bib122","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jhydrol.2014.11.040","article-title":"A review of foundational methods for checking the structural identifiability of models: results for rainfall-runoff","volume":"520","author":"Shin","year":"2015","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.107007_bib123","first-page":"9391","article-title":"Reliable post hoc explanations: modeling uncertainty in explainability","volume":"34","author":"Slack","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.envsoft.2026.107007_bib124","series-title":"Mathematical Modelling and Computational Experiment, Translated from Russian: I","article-title":"Sensitivity analysis for non-linear mathematical models","author":"Sobol\u2019","year":"1993"},{"issue":"7","key":"10.1016\/j.envsoft.2026.107007_bib126","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1016\/j.cpc.2010.03.006","article-title":"A new derivative based importance criterion for groups of variables and its link with the global sensitivity indices","volume":"181","author":"Sobol\u2019","year":"2010","journal-title":"Comput. Phys. Commun."},{"issue":"1","key":"10.1016\/j.envsoft.2026.107007_bib127","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1137\/15M1048070","article-title":"Shapley effects for global sensitivity analysis: theory and computation","volume":"4","author":"Song","year":"2016","journal-title":"SIAM\/ASA J. Uncertain. Quantification"},{"issue":"1","key":"10.1016\/j.envsoft.2026.107007_bib128","doi-asserted-by":"crossref","first-page":"72","DOI":"10.2307\/1412159","article-title":"The proof and measurement of association between two things","volume":"15","author":"Spearman","year":"1904","journal-title":"Am. J. Psychol."},{"key":"10.1016\/j.envsoft.2026.107007_bib129","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolind.2021.107612","article-title":"Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh","volume":"126","author":"Talukdar","year":"2021","journal-title":"Ecol. Indic."},{"key":"10.1016\/j.envsoft.2026.107007_bib130","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s00477-001-0085-x","article-title":"Can global sensitivity analysis steer the implementation of models for environmental assessments and decision making?","volume":"16","author":"Tarantola","year":"2002","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"10.1016\/j.envsoft.2026.107007_bib131","doi-asserted-by":"crossref","DOI":"10.1029\/2005WR003995","article-title":"A hybrid regularized inversion methodology for highly parameterized environmental models: hybrid regularization methodology","volume":"41","author":"Tonkin","year":"2005","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.107007_bib132","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.neucom.2017.05.104","article-title":"Regression tree ensembles for wind energy and solar radiation prediction","volume":"326","author":"Torres-Barr\u00e1n","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.envsoft.2026.107007_bib133","first-page":"1","article-title":"Explainable AI: current status and future potential","author":"van der Velden","year":"2023","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.envsoft.2026.107007_bib134","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.jhydrol.2014.12.056","article-title":"Global sensitivity analysis for urban water quality modelling: terminology, convergence and comparison of different methods","volume":"522","author":"Vanrolleghem","year":"2015","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.107007_bib135","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1016\/j.apenergy.2017.12.125","article-title":"Energy harvesting technologies in roadway and bridge for different applications\u2013A comprehensive review","volume":"212","author":"Wang","year":"2018","journal-title":"Appl. Energy"},{"key":"10.1016\/j.envsoft.2026.107007_bib136","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.chemosphere.2018.07.059","article-title":"Early chemical and toxicological risk characterization of inorganic constituents in surface water from the Canadian oil sands first large-scale end pit lake","volume":"211","author":"White","year":"2018","journal-title":"Chemosphere"},{"key":"10.1016\/j.envsoft.2026.107007_bib137","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.aquatox.2013.09.003","article-title":"Endocrine disruption and oxidative stress in larvae of Chironomus dilutus following short-term exposure to fresh or aged oil sands process-affected water","volume":"142","author":"Wiseman","year":"2013","journal-title":"Aquatic toxicology"},{"issue":"2","key":"10.1016\/j.envsoft.2026.107007_bib138","doi-asserted-by":"crossref","DOI":"10.1016\/j.cbpc.2012.12.002","article-title":"Transcriptional responses of male fathead minnows exposed to oil sands process-affected water","volume":"157","author":"Wiseman","year":"2013","journal-title":"Comp. Biochem. Physiol. C Toxicol. Pharmacol."},{"issue":"4","key":"10.1016\/j.envsoft.2026.107007_bib139","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1097\/EDE.0000000000001027","article-title":"Can hyperparameter tuning improve the performance of a super learner?: a case study","volume":"30","author":"Wong","year":"2019","journal-title":"Epidemiology"},{"issue":"1","key":"10.1016\/j.envsoft.2026.107007_bib140","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.csda.2010.06.028","article-title":"Understanding and comparisons of different sampling approaches for the fourier amplitudes sensitivity test (FAST)","volume":"55","author":"Xu","year":"2011","journal-title":"Computational statistics & data analysis"},{"issue":"3","key":"10.1016\/j.envsoft.2026.107007_bib141","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s44230-023-00038-y","article-title":"Survey on explainable AI: from approaches, limitations and applications aspects","volume":"3","author":"Yang","year":"2023","journal-title":"Human-Centric Intelligent Systems"},{"issue":"4","key":"10.1016\/j.envsoft.2026.107007_bib142","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.envsoft.2010.10.007","article-title":"Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis","volume":"26","author":"Yang","year":"2011","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.envsoft.2026.107007_bib143","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2021.112657","article-title":"Turbidity prediction of lake-type raw water using random forest model based on meteorological data: a case study of Tai lake, China","volume":"290","author":"Zhang","year":"2021","journal-title":"J. Environ. Manag."},{"key":"10.1016\/j.envsoft.2026.107007_bib144","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2023.100326","article-title":"An explainable AI model for power plant NOx emission control","volume":"15","author":"Zhou","year":"2024","journal-title":"Energy AI"},{"key":"10.1016\/j.envsoft.2026.107007_bib145","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.scitotenv.2012.04.024","article-title":"Petroleum coke adsorption as a water management option for oil sands process-affected water","volume":"427","author":"Zubot","year":"2012","journal-title":"Sci. Total Environ."}],"container-title":["Environmental Modelling & Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226001544?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226001544?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T02:40:13Z","timestamp":1780540813000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1364815226001544"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":143,"alternative-id":["S1364815226001544"],"URL":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.107007","relation":{},"ISSN":["1364-8152"],"issn-type":[{"value":"1364-8152","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"On the complementary roles of XVARS and SHAP in explainable AI","name":"articletitle","label":"Article Title"},{"value":"Environmental Modelling & Software","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.107007","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"107007"}}