{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T01:09:42Z","timestamp":1778807382409,"version":"3.51.4"},"reference-count":69,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Modelling & Software"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.envsoft.2026.106986","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T15:50:50Z","timestamp":1776959450000},"page":"106986","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Interpretable residual-informed hybrid physics-informed machine learning framework for runoff prediction in a typical karst basin in southwest China"],"prefix":"10.1016","volume":"202","author":[{"given":"Chongxun","family":"Mo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changhao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiameng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yugong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.envsoft.2026.106986_bib1","doi-asserted-by":"crossref","DOI":"10.3390\/w15050954","article-title":"A review of the application of the soil and water assessment tool (SWAT) in karst basins","volume":"15","author":"Al Khoury","year":"2023","journal-title":"Water"},{"key":"10.1016\/j.envsoft.2026.106986_bib2","series-title":"2018 3RD INTERNATIONAL CONFERENCE ON POWER AND RENEWABLE ENERGY (ICPRE). Berlin, GERMANY","article-title":"Particle swarm optimized-support vector regression hybrid model for daily horizon electricity demand forecasting using climate dataset","author":"Al-Musaylh","year":"2018"},{"issue":"3","key":"10.1016\/j.envsoft.2026.106986_bib3","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s40722-025-00394-w","article-title":"Predicting near-real-time total water level with an artificial intelligence model based on australia's tidal wave energy belt dataset","volume":"11","author":"Al-Musaylh","year":"2025","journal-title":"JOURNAL OF OCEAN ENGINEERING AND MARINE ENERGY"},{"issue":"9","key":"10.1016\/j.envsoft.2026.106986_bib4","doi-asserted-by":"crossref","first-page":"3369","DOI":"10.1007\/s11269-024-03819-7","article-title":"Reservoir risk operation of domestic-production-ecology water supply based on runoff forecast uncertainty","volume":"38","author":"Bai","year":"2024","journal-title":"Water Resour. Manag."},{"key":"10.1016\/j.envsoft.2026.106986_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2022.103667","article-title":"Relation between prognostics predictor evaluation metrics and local interpretability SHAP values","volume":"306","author":"Baptista","year":"2022","journal-title":"Artif. Intell."},{"key":"10.1016\/j.envsoft.2026.106986_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2022.128618","article-title":"Enhancing predictive skills in a physically consistent way: Physics-Informed Machine Learning for hydrological processes","volume":"615","author":"Bhasme","year":"2022","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.106986_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.130091","article-title":"Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM: a case study for runoff simulation in an arid area","volume":"625","author":"Bian","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.106986_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.130098","article-title":"Hydrological models weighting for hydrological projections: the impacts on future peak flows","volume":"625","author":"Castaneda-Gonzalez","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.106986_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125744","article-title":"Study on runoff forecasting and error correction driven by atmosphere-ocean-land dataset","volume":"263","author":"Chang","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.envsoft.2026.106986_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.132392","article-title":"Adaptive assessment of reservoir operation to hydrometeorological comprehensive dry and wet condition evolution in a multi-reservoir region of southeastern China","volume":"648","author":"Chen","year":"2025","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.106986_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2021.110666","article-title":"Physics-informed machine learning for reduced-order modeling of nonlinear problems","volume":"446","author":"Chen","year":"2021","journal-title":"J. Comput. Phys."},{"issue":"1","key":"10.1016\/j.envsoft.2026.106986_bib13","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s11069-014-1496-6","article-title":"The failed-levee effect: do societies learn from flood disasters?","volume":"76","author":"Collenteur","year":"2015","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.envsoft.2026.106986_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2024.121299","article-title":"Assessment of monthly runoff simulations based on a physics-informed machine learning framework: the effect of intermediate variables in its construction","volume":"362","author":"Deng","year":"2024","journal-title":"J. Environ. Manag."},{"issue":"3","key":"10.1016\/j.envsoft.2026.106986_bib15","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1007\/s11069-022-05363-2","article-title":"Deep insight into daily runoff forecasting based on a CNN-LSTM model","volume":"113","author":"Deng","year":"2022","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.envsoft.2026.106986_bib16","article-title":"Prediction of significant wave height using a VMD-LSTM-rolling model in the South Sea of China","volume":"11","author":"Ding","year":"2024","journal-title":"Front. Mar. Sci."},{"issue":"124763A","key":"10.1016\/j.envsoft.2026.106986_bib17","article-title":"Explainable deeply-fused nets electricity demand prediction model: factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts","volume":"378","author":"Ghimire","year":"2025","journal-title":"Appl. Energy"},{"issue":"100302","key":"10.1016\/j.envsoft.2026.106986_bib18","article-title":"Integrated Multi-Head Self-Attention Transformer model for electricity demand prediction incorporating local climate variables","volume":"14","author":"Ghimire","year":"2023","journal-title":"Energy AI"},{"key":"10.1016\/j.envsoft.2026.106986_bib19","first-page":"107","article-title":"Problems of flood and drought in a typical peak cluster depression Karst Area (SW China)","author":"Guo","year":"2010","journal-title":"inMalaga, SpainER"},{"key":"10.1016\/j.envsoft.2026.106986_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131085","article-title":"Hydrogeological structures of karst features using hydrographs in an underground river basin formed in a peak cluster depression, southwest China","volume":"634","author":"Guo","year":"2024","journal-title":"J. Hydrol."},{"issue":"7","key":"10.1016\/j.envsoft.2026.106986_bib21","doi-asserted-by":"crossref","DOI":"10.3390\/w17071095","article-title":"A comprehensive review and application of Bayesian methods in hydrological modelling: past, present, and future directions","volume":"17","author":"Haddad","year":"2025","journal-title":"Water"},{"key":"10.1016\/j.envsoft.2026.106986_bib22","article-title":"Improving long-term multivariate time series forecasting with a seasonal-trend decomposition-based 2-dimensional temporal convolution dense network","volume":"14","author":"Hao","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.envsoft.2026.106986_bib23","doi-asserted-by":"crossref","DOI":"10.1029\/2021WR031817","article-title":"Global evaluation of runoff simulation from climate, hydrological and land surface models","volume":"59","author":"Hou","year":"2023","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106986_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2025.104079","article-title":"Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling","volume":"25","author":"Houenafa","year":"2025","journal-title":"Results Eng."},{"issue":"15","key":"10.1016\/j.envsoft.2026.106986_bib25","doi-asserted-by":"crossref","first-page":"6173","DOI":"10.1007\/s11269-024-03953-2","article-title":"Improving sub-daily runoff forecast based on the multi-objective optimized extreme learning machine for Reservoir operation","volume":"38","author":"Jia","year":"2024","journal-title":"Water Resour. Manag."},{"key":"10.1016\/j.envsoft.2026.106986_bib26","series-title":"An Improved Time-Series Forecasting Model Using Time Series Decomposition and GRU Architecture","author":"Joo","year":"2022"},{"key":"10.1016\/j.envsoft.2026.106986_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2022.128504","article-title":"Elaborate simulations of floods in a karst trough valley basin with the simplified Karst-Liuxihe model","volume":"614","author":"Li","year":"2022","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.106986_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.129603","article-title":"Enhancing streamflow estimation by integrating a data-driven evapotranspiration submodel into process-based hydrological models","volume":"621","author":"Lian","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.106986_bib30","article-title":"A stacking ensemble model of various machine learning models for daily runoff forecasting","volume":"15","author":"Lu","year":"2023","journal-title":"Water"},{"issue":"271818","key":"10.1016\/j.envsoft.2026.106986_bib31","article-title":"Runoff forecast model integrating time series decomposition and deep learning for the short term: a case Study in the weihe River Basin, China","volume":"17","author":"Ma","year":"2025","journal-title":"Water"},{"key":"10.1016\/j.envsoft.2026.106986_bib32","doi-asserted-by":"crossref","DOI":"10.1029\/2019MS001751","article-title":"Assessment of model drifts in seasonal forecasting: sensitivity to ensemble size and implications for bias correction","volume":"12","author":"Manzanas","year":"2020","journal-title":"J. Adv. Model. Earth Syst."},{"key":"10.1016\/j.envsoft.2026.106986_bib33","doi-asserted-by":"crossref","DOI":"10.1088\/1748-9326\/ad98aa","article-title":"Changes in high-latitude surface energy balance driven by snowpack and vegetation dynamics under warmer climate","volume":"20","author":"Mekonnen","year":"2025","journal-title":"Environ. Res. Lett."},{"issue":"6","key":"10.1016\/j.envsoft.2026.106986_bib34","doi-asserted-by":"crossref","first-page":"2601","DOI":"10.5194\/hess-23-2601-2019","article-title":"On the choice of calibration metrics for high-flow estimation using hydrologic models","volume":"23","author":"Mizukami","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"881","key":"10.1016\/j.envsoft.2026.106986_bib35","article-title":"Combining standard artificial intelligence models, pre-processing techniques, and post-processing methods to improve the accuracy of monthly runoff predictions in Karst-Area watersheds","volume":"13","author":"Mo","year":"2023","journal-title":"APPLIED SCIENCES-BASEL"},{"issue":"56939","key":"10.1016\/j.envsoft.2026.106986_bib36","article-title":"Investigation of the EWT-PSO-SVM model for runoff forecasting in the karst area","volume":"13","author":"Mo","year":"2023","journal-title":"APPLIED SCIENCES-BASEL"},{"issue":"9","key":"10.1016\/j.envsoft.2026.106986_bib37","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1175\/JHM-D-20-0072.1","article-title":"Robustness of process-based versus data-driven modeling in changing climatic conditions","volume":"21","author":"O","year":"2020","journal-title":"J. Hydrometeorol."},{"issue":"8","key":"10.1016\/j.envsoft.2026.106986_bib38","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1890\/060119.1","article-title":"On the potential for high-resolution lidar to improve rainfall interception estimates in forest ecosystems","volume":"5","author":"Roth","year":"2007","journal-title":"Front. Ecol. Environ."},{"key":"10.1016\/j.envsoft.2026.106986_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2025.110081","article-title":"Multifactor interpretability method for offshore wind power output prediction based on TPE-CatBoost-SHAP","volume":"123","author":"Ruan","year":"2025","journal-title":"Comput. Electr. Eng."},{"issue":"2517","key":"10.1016\/j.envsoft.2026.106986_bib40","article-title":"Machine learning models coupled with variational mode decomposition: a new approach for modeling daily rainfall-runoff","volume":"9","author":"Seo","year":"2018","journal-title":"Atmosphere"},{"issue":"10","key":"10.1016\/j.envsoft.2026.106986_bib41","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1007\/s11269-011-9828-8","article-title":"Evaluating the SWAT model for hydrological modeling in the Xixian Basin and a comparison with the XAJ model","volume":"25","author":"Shi","year":"2011","journal-title":"Water Resour. Manag."},{"key":"10.1016\/j.envsoft.2026.106986_bib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2025.106636","article-title":"Hybrid-GR4J: a hybrid hydrological model integrating GR4J and deep learning","volume":"193","author":"Sun","year":"2025","journal-title":"Environ. Modell. Software"},{"issue":"1","key":"10.1016\/j.envsoft.2026.106986_bib43","article-title":"Ensemble streamflow forecasting based on variational mode decomposition and long short term memory","volume":"12","author":"Sun","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.envsoft.2026.106986_bib44","doi-asserted-by":"crossref","DOI":"10.1029\/2024WR039525","article-title":"On using AI-Based large-sample emulators for Land\/Hydrology model calibration and regionalization","volume":"61","author":"Tang","year":"2025","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.envsoft.2026.106986_bib45","doi-asserted-by":"crossref","DOI":"10.3390\/su12156006","article-title":"Using disaster outcomes to validate components of social vulnerability to floods: flood deaths and property damage across the USA","volume":"12","author":"Tellman","year":"2020","journal-title":"Sustainability"},{"key":"10.1016\/j.envsoft.2026.106986_bib46","series-title":"2024 13th International Conference on Renewable Energy Research and Applications (ICRERA) ER","article-title":"Feature selection approaches for short-term solar photovoltaic power forecasting","author":"Thipwangmek","year":"2024"},{"key":"10.1016\/j.envsoft.2026.106986_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.130458","article-title":"Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions","volume":"628","author":"Tripathy","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.106986_bib48","article-title":"SMGformer: integrating STL and multi-head self-attention in deep learning model for multi-step runoff forecasting","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."},{"issue":"311","key":"10.1016\/j.envsoft.2026.106986_bib49","article-title":"A singular spectrum analysis-enhanced BiTCN-selfattention model for runoff prediction","volume":"18","author":"Wang","year":"2025","journal-title":"Earth Sci. Inf."},{"issue":"9","key":"10.1016\/j.envsoft.2026.106986_bib50","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1080\/02626667.2021.1937631","article-title":"An adaptive daily runoff forecast model using VMD-LSTM-PSO hybrid approach","volume":"66","author":"Wang","year":"2021","journal-title":"Hydrol. Sci. J."},{"key":"10.1016\/j.envsoft.2026.106986_bib51","article-title":"Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF\/NBAR\/albedo product","volume":"59104\u2013117","author":"Wang","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10.1016\/j.envsoft.2026.106986_bib52","doi-asserted-by":"crossref","DOI":"10.3390\/w15213717","article-title":"Monthly runoff prediction by combined models based on secondary decomposition at the wulong hydrological station in the Yangtze River Basin","volume":"15","author":"Wei","year":"2023","journal-title":"Water"},{"key":"10.1016\/j.envsoft.2026.106986_bib53","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131992","article-title":"Error correction method based on deep learning for improving the accuracy of conceptual rainfall-runoff model","volume":"643","author":"Wenchuan","year":"2024","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.106986_bib54","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2022.115671","article-title":"A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks","volume":"403","author":"Wu","year":"2023","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.envsoft.2026.106986_bib55","doi-asserted-by":"crossref","DOI":"10.3390\/rs11212468","article-title":"Estimating rainfall interception of vegetation canopy from MODIS imageries in Southern China","volume":"11","author":"Wu","year":"2019","journal-title":"Remote Sens."},{"key":"10.1016\/j.envsoft.2026.106986_bib57","doi-asserted-by":"crossref","DOI":"10.3390\/hydrology11070090","article-title":"A modified Xinanjiang model for quantifying streamflow components in a typical Basin in Eastern China","volume":"11","author":"Wu","year":"2024","journal-title":"Hydrology"},{"issue":"2","key":"10.1016\/j.envsoft.2026.106986_bib58","doi-asserted-by":"crossref","DOI":"10.3390\/w13020139","article-title":"Deterministic analysis and uncertainty analysis of ensemble forecasting model based on variational mode decomposition for estimation of monthly groundwater level","volume":"13","author":"Wu","year":"2021","journal-title":"Water"},{"issue":"11\u201312","key":"10.1016\/j.envsoft.2026.106986_bib59","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1007\/s00170-005-0001-5","article-title":"Enhanced STL","volume":"29","author":"Wu","year":"2006","journal-title":"INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY"},{"key":"10.1016\/j.envsoft.2026.106986_bib60","article-title":"Comprehensive analysis for long-term hydrological simulation by deep learning techniques and remote sensing","volume":"10","author":"Xu","year":"2022","journal-title":"Front. Earth Sci."},{"issue":"1","key":"10.1016\/j.envsoft.2026.106986_bib61","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2166\/hydro.2023.216","article-title":"Improved monthly runoff time series prediction using the CABES-LSTM mixture model based on CEEMDAN-VMD decomposition","volume":"26","author":"Xu","year":"2024","journal-title":"J. Hydroinform."},{"issue":"6","key":"10.1016\/j.envsoft.2026.106986_bib62","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1007\/s11269-023-03731-6","article-title":"Evaluation and interpretation of runoff forecasting models based on hybrid deep neural networks","volume":"38","author":"Yang","year":"2024","journal-title":"Water Resour. Manag."},{"issue":"129115C","key":"10.1016\/j.envsoft.2026.106986_bib63","article-title":"Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese Loess Plateau","volume":"617","author":"Yu","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envsoft.2026.106986_bib64","doi-asserted-by":"crossref","DOI":"10.3390\/w12082201","article-title":"Separating the impacts of climate change and human activities on runoff: a review of method and application","volume":"12","author":"Zeng","year":"2020","journal-title":"Water"},{"key":"10.1016\/j.envsoft.2026.106986_bib65","first-page":"605219","article-title":"Contributions of climate change and human activities to runoff change in seven typical catchments across China","author":"Zhai","year":"2017","journal-title":"Sci. Total Environ."},{"issue":"131238","key":"10.1016\/j.envsoft.2026.106986_bib66","article-title":"Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing","volume":"636","author":"Zhang","year":"2024","journal-title":"J. Hydrol."},{"issue":"1","key":"10.1016\/j.envsoft.2026.106986_bib67","article-title":"Comparative study of rainfall prediction based on different decomposition methods of VMD","volume":"13","author":"Zhang","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.envsoft.2026.106986_bib68","article-title":"Impact of Climate Change and Human Activities to Runoff in the Du River Basin of the Qinling-Daba Mountains, China","volume":"15","author":"Zhang","year":"2023","journal-title":"Remote Sens."},{"issue":"5","key":"10.1016\/j.envsoft.2026.106986_bib69","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1109\/TETCI.2021.3100641","article-title":"A Survey on neural network interpretability","volume":"5","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"10.1016\/j.envsoft.2026.106986_bib70","first-page":"814642","article-title":"Electric load forecasting by hybrid self-recurrent support vector regression model with variational mode decomposition and improved cuckoo search Algorithm","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.envsoft.2026.106986_bib71","article-title":"A comprehensive review of methods for hydrological forecasting based on deep learning","volume":"16","author":"Zhao","year":"2024","journal-title":"Water"},{"issue":"6","key":"10.1016\/j.envsoft.2026.106986_bib72","doi-asserted-by":"crossref","DOI":"10.3390\/rs13061057","article-title":"Assimilation of multi-source precipitation data over Southeast China using a nonparametric framework","volume":"13","author":"Zhou","year":"2021","journal-title":"Remote Sens."}],"container-title":["Environmental Modelling & Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226001337?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1364815226001337?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:15:11Z","timestamp":1778804111000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1364815226001337"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":69,"alternative-id":["S1364815226001337"],"URL":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.106986","relation":{},"ISSN":["1364-8152"],"issn-type":[{"value":"1364-8152","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Interpretable residual-informed hybrid physics-informed machine learning framework for runoff prediction in a typical karst basin in southwest China","name":"articletitle","label":"Article Title"},{"value":"Environmental Modelling & Software","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envsoft.2026.106986","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":"106986"}}