{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T12:11:11Z","timestamp":1778933471671,"version":"3.51.4"},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Systems with Applications"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.iswa.2026.200666","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T23:24:54Z","timestamp":1776295494000},"page":"200666","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A novel hyper-tuned hybrid deep learning architecture for extended forecasting horizons: Application for soil moisture"],"prefix":"10.1016","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8750-9269","authenticated-orcid":false,"given":"Ayla","family":"Amamou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lilia","family":"Sidhom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelkader","family":"Mami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.iswa.2026.200666_bib0001","doi-asserted-by":"crossref","first-page":"554","DOI":"10.3390\/rs13040554","article-title":"Deep learning forecasts of soil moisture: Convolutional neural network and gated recurrent unit models coupled with satellite-derived MODIS, observations and synoptic-scale climate index data","volume":"13","author":"Ahmed","year":"2021","journal-title":"Remote Sensing"},{"key":"10.1016\/j.iswa.2026.200666_bib0002","doi-asserted-by":"crossref","first-page":"230","DOI":"10.3390\/a13090230","article-title":"Simulated annealing with exploratory sensing for global optimization","volume":"13","author":"Almarashi","year":"2020","journal-title":"Algorithms"},{"key":"10.1016\/j.iswa.2026.200666_bib0003","series-title":"Presented at the 2025 IEEE 8th Congress on Information Science and Technology (CiSt)","first-page":"304","article-title":"Accuracy-efficiency trade-offs in smart models for soil moisture","author":"Amamou","year":"2025"},{"key":"10.1016\/j.iswa.2026.200666_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106632","article-title":"Sustainability and predictive accuracy evaluation of gel and embroidered electrodes for ECG monitoring","volume":"96","author":"Ben Othman","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.iswa.2026.200666_bib0005","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.3390\/math11071745","article-title":"Levy flight-based improved Grey Wolf optimization: A solution for various engineering problems","volume":"11","author":"Bhatt","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.iswa.2026.200666_bib0006","series-title":"A very british affair","first-page":"161","article-title":"Box and Jenkins: Time series analysis, forecasting and control","author":"Box","year":"2013"},{"key":"10.1016\/j.iswa.2026.200666_bib0007","series-title":"Convex optimization","author":"Boyd","year":"2004"},{"key":"10.1016\/j.iswa.2026.200666_bib0008","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0214508","article-title":"Research on soil moisture prediction model based on deep learning","volume":"14","author":"Cai","year":"2019","journal-title":"PloS one"},{"key":"10.1016\/j.iswa.2026.200666_bib0009","doi-asserted-by":"crossref","first-page":"4071","DOI":"10.3390\/rs16214071","article-title":"A multi-scale feature fusion deep learning network for the extraction of cropland based on landsat data","volume":"16","author":"Chen","year":"2024","journal-title":"Remote Sensing"},{"key":"10.1016\/j.iswa.2026.200666_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2025.115999","article-title":"Novel NSGA-II optimized LSTM and GRU models for short-term forecasting of residential heating load","volume":"344","author":"Deka","year":"2025","journal-title":"Energy and Buildings"},{"key":"10.1016\/j.iswa.2026.200666_bib0011","doi-asserted-by":"crossref","first-page":"210","DOI":"10.3390\/agriculture14020210","article-title":"A hybrid LSTM approach for irrigation scheduling in maize crop","volume":"14","author":"Dolaptsis","year":"2024","journal-title":"Agriculture"},{"key":"10.1016\/j.iswa.2026.200666_bib0012","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2017.07.001","article-title":"ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions","volume":"203","author":"Dorigo","year":"2017","journal-title":"Remote Sensing of Environment"},{"key":"10.1016\/j.iswa.2026.200666_bib0013","article-title":"Air pollution prediction using LSTM deep learning and metaheuristics algorithms","volume":"24","author":"Drewil","year":"2022","journal-title":"Measurement: Sensors"},{"key":"10.1016\/j.iswa.2026.200666_bib0014","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ijrefrig.2007.06.007","article-title":"Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems","volume":"31","author":"Esen","year":"2008","journal-title":"International Journal of Refrigeration"},{"key":"10.1016\/j.iswa.2026.200666_bib0015","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1016\/j.buildenv.2008.01.002","article-title":"Predicting performance of a ground-source heat pump system using fuzzy weighted pre-processing-based ANFIS","volume":"43","author":"Esen","year":"2008","journal-title":"Building and Environment"},{"key":"10.1016\/j.iswa.2026.200666_bib0016","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.ijthermalsci.2007.03.004","article-title":"Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing","volume":"47","author":"Esen","year":"2008","journal-title":"International Journal of Thermal Sciences"},{"key":"10.1016\/j.iswa.2026.200666_bib0017","doi-asserted-by":"crossref","first-page":"10673","DOI":"10.1016\/j.eswa.2009.02.045","article-title":"Modelling of a new solar air heater through least-squares support vector machines","volume":"36","author":"Esen","year":"2009","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.iswa.2026.200666_bib0018","series-title":"Automated machine learning: Methods, systems, challenges","first-page":"3","article-title":"Hyperparameter optimization","author":"Feurer","year":"2019"},{"key":"10.1016\/j.iswa.2026.200666_bib0019","doi-asserted-by":"crossref","first-page":"4667","DOI":"10.3390\/en17184667","article-title":"Short-term electrical load forecasting based on IDBO-PTCN-GRU model","volume":"17","author":"Gong","year":"2024","journal-title":"Energies"},{"key":"10.1016\/j.iswa.2026.200666_bib0020","doi-asserted-by":"crossref","first-page":"5493","DOI":"10.1002\/2015GL064127","article-title":"Assessment of future changes in water availability and aridity","volume":"42","author":"Greve","year":"2015","journal-title":"Geophysical Research Letters"},{"key":"10.1016\/j.iswa.2026.200666_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106971","article-title":"A hybrid model for the prediction of dissolved oxygen in seabass farming","volume":"198","author":"Guo","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.iswa.2026.200666_bib0022","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.3390\/pr12061063","article-title":"A gated recurrent unit model with fibonacci attenuation particle swarm optimization for carbon emission prediction","volume":"12","author":"Guo","year":"2024","journal-title":"Processes"},{"key":"10.1016\/j.iswa.2026.200666_bib0023","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2015. Deep residual learning for image recognition. https:\/\/doi.org\/10.48550\/ARXIV.1512.03385.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.iswa.2026.200666_bib0024","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"10.1016\/j.iswa.2026.200666_bib0025","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.3390\/w16213133","article-title":"Forecasting multi-step soil moisture with three-phase hybrid wavelet-least absolute shrinkage selection operator-long short-term memory network (moDWT-Lasso-LSTM) model","volume":"16","author":"Jayasinghe","year":"2024","journal-title":"Water"},{"key":"10.1016\/j.iswa.2026.200666_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.iswa.2025.200528","article-title":"Enhancing CNN-based network intrusion detection through hyperparameter optimization","volume":"26","author":"Kaissar","year":"2025","journal-title":"Intelligent Systems with Applications"},{"key":"10.1016\/j.iswa.2026.200666_bib0027","doi-asserted-by":"crossref","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","article-title":"A review on genetic algorithm: Past, present, and future","volume":"80","author":"Katoch","year":"2021","journal-title":"Multimedia Tools and Applications"},{"key":"10.1016\/j.iswa.2026.200666_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.iswa.2025.200564","article-title":"STL-ELM: A computationally efficient hybrid approach for predicting high volatility stock market","volume":"27","author":"Kehinde","year":"2025","journal-title":"Intelligent Systems with Applications"},{"key":"10.1016\/j.iswa.2026.200666_bib0029","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1080\/02626667.2025.2581264","article-title":"Enhancing long-term water quality forecasting with a hybrid deep-learning approach integrating MODWT, CNN, and GRU","volume":"71","author":"Khosravi","year":"2026","journal-title":"Hydrological Sciences Journal"},{"key":"10.1016\/j.iswa.2026.200666_bib0030","series-title":"2017 IEEE International Conference on Computer Vision (ICCV). Presented at the 2017 IEEE International Conference on Computer Vision (ICCV)","first-page":"4990","article-title":"RDFNet: RGB-D multi-level residual feature fusion for indoor semantic segmentation","author":"Lee","year":"2017"},{"key":"10.1016\/j.iswa.2026.200666_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.geoderma.2021.115651","article-title":"An attention-aware LSTM model for soil moisture and soil temperature prediction","volume":"409","author":"Li","year":"2022","journal-title":"Geoderma"},{"key":"10.1016\/j.iswa.2026.200666_bib0032","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.3390\/w16101376","article-title":"Enhancing soil moisture forecasting accuracy with REDF-LSTM: Integrating residual en-decoding and feature attention mechanisms","volume":"16","author":"Li","year":"2024","journal-title":"Water"},{"key":"10.1016\/j.iswa.2026.200666_bib0033","article-title":"Time series forecasting with deep learning: A survey","volume":"379","author":"Lim","year":"2021","journal-title":"Philosophical Transactions of the Royal Society A"},{"key":"10.1016\/j.iswa.2026.200666_bib0034","article-title":"A multiscale deep learning model for soil moisture integrating satellite and In situ data","volume":"49","author":"Liu","year":"2022","journal-title":"Geophysical Research Letters"},{"key":"10.1016\/j.iswa.2026.200666_bib0035","article-title":"Short-term traffic flow forecasting based on a novel combined model","volume":"16","author":"Liu","year":"2024","journal-title":"Sustainability"},{"key":"10.1016\/j.iswa.2026.200666_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2024.118122","article-title":"A hybrid deep learning model based on parallel architecture TCN-LSTM with Savitzky-Golay filter for wind power prediction","volume":"302","author":"Liu","year":"2024","journal-title":"Energy Conversion and Management"},{"key":"10.1016\/j.iswa.2026.200666_bib0037","doi-asserted-by":"crossref","first-page":"36571","DOI":"10.1109\/ACCESS.2021.3062776","article-title":"An effective hybrid NARX-LSTM model for point and interval PV power forecasting","volume":"9","author":"Massaoudi","year":"2021","journal-title":"IEEE Access: Practical Innovations, Open Solutions"},{"key":"10.1016\/j.iswa.2026.200666_bib0038","doi-asserted-by":"crossref","first-page":"13187","DOI":"10.1007\/s10462-023-10470-y","article-title":"An exhaustive review of the metaheuristic algorithms for search and optimization: Taxonomy, applications, and open challenges","volume":"56","author":"Rajwar","year":"2023","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.iswa.2026.200666_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2026.109391","article-title":"Techno-economic multi-objective planning of hydrogen-enabled hybrid microgrids: A realistic case study","volume":"29","author":"Refaat","year":"2026","journal-title":"Results in Engineering"},{"key":"10.1016\/j.iswa.2026.200666_bib0040","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1175\/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2","article-title":"Hydrologic data assimilation with the ensemble Kalman filter","volume":"130","author":"Reichle","year":"2002","journal-title":"Monthly Weather Review"},{"key":"10.1016\/j.iswa.2026.200666_bib0041","doi-asserted-by":"crossref","first-page":"432","DOI":"10.3390\/info16060432","article-title":"Clinical applicability and cross-dataset validation of machine learning models for binary glaucoma detection","volume":"16","author":"Remyes","year":"2025","journal-title":"Information"},{"key":"10.1016\/j.iswa.2026.200666_bib0042","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1186\/s40001-025-02680-7","article-title":"Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: A comprehensive review of machine learning and deep learning approaches","volume":"30","author":"Sadr","year":"2025","journal-title":"European Journal of Medical Research"},{"key":"10.1016\/j.iswa.2026.200666_bib0043","doi-asserted-by":"crossref","first-page":"18447","DOI":"10.48084\/etasr.9047","article-title":"An ensemble forecasting method based on optimized LSTM and GRU for temperature and humidity forecasting","volume":"14","author":"Saleem","year":"2024","journal-title":"Engineering, Technology & Applied Science Research"},{"key":"10.1016\/j.iswa.2026.200666_bib0044","doi-asserted-by":"crossref","first-page":"316","DOI":"10.3390\/make6010016","article-title":"SHapley additive exPlanations (SHAP) for efficient feature selection in rolling bearing fault diagnosis","volume":"6","author":"Santos","year":"2024","journal-title":"Machine Learning and Knowledge Extraction"},{"key":"10.1016\/j.iswa.2026.200666_bib0045","unstructured":"Sha, X., 2024. Time series stock price forecasting based on genetic algorithm (GA)-long short-term memory network (LSTM) optimization. arXiv. 2024 doi: 10.54254\/2754-1169\/91\/20241031.2405.03151. https:\/\/doi.org\/10.48550\/ARXIV.2405.03151."},{"key":"10.1016\/j.iswa.2026.200666_bib0046","doi-asserted-by":"crossref","first-page":"62840","DOI":"10.1109\/ACCESS.2023.3287319","article-title":"A hybrid model based on complete ensemble empirical mode decomposition with adaptive noise, GRU network and whale optimization algorithm for wind power prediction","volume":"11","author":"Sheng","year":"2023","journal-title":"IEEE Access : Practical Innovations, Open Solutions"},{"key":"10.1016\/j.iswa.2026.200666_bib0047","doi-asserted-by":"crossref","first-page":"66965","DOI":"10.1109\/ACCESS.2021.3076313","article-title":"Short-term load forecasting based on Adabelief optimized temporal convolutional network and gated recurrent unit hybrid neural network","volume":"9","author":"Shi","year":"2021","journal-title":"IEEE Access : Practical Innovations, Open Solutions"},{"key":"10.1016\/j.iswa.2026.200666_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2026.100712","article-title":"Early deployment of deep learning models for lithium-ion battery state-of-health prediction with limited initial data","volume":"24","author":"Sim","year":"2026","journal-title":"Energy and AI"},{"key":"10.1016\/j.iswa.2026.200666_bib0049","doi-asserted-by":"crossref","first-page":"857","DOI":"10.3390\/axioms13120857","article-title":"A novel sine step size for warm-restart stochastic gradient descent","volume":"13","author":"Soheil Shamaee","year":"2024","journal-title":"Axioms"},{"key":"10.1016\/j.iswa.2026.200666_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107532","article-title":"Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems","volume":"128","author":"Sowmya","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.iswa.2026.200666_bib0051","doi-asserted-by":"crossref","DOI":"10.1186\/s13638-023-02275-y","article-title":"A novel deep learning automatic modulation classifier with fusion of multichannel information using GRU","author":"Sun","year":"2023","journal-title":"Journal of Wireless Communications and Networking"},{"key":"10.1016\/j.iswa.2026.200666_bib0052","series-title":"CVPR 2011. Presented at the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1521","article-title":"Unbiased look at dataset bias","author":"Torralba","year":"2011"},{"key":"10.1016\/j.iswa.2026.200666_bib0053","doi-asserted-by":"crossref","first-page":"8849","DOI":"10.3390\/su17198849","article-title":"Performance comparison of metaheuristic and hybrid algorithms used for energy cost minimization in a solar\u2013Wind\u2013Battery microgrid","volume":"17","author":"Vadi","year":"2025","journal-title":"Sustainability"},{"key":"10.1016\/j.iswa.2026.200666_bib0054","article-title":"A practical temporal transfer learning model for multi-step water quality index forecasting using A CNN-coupled dual-path LSTM network","volume":"60","author":"Wai","year":"2025","journal-title":"Journal of Hydrology: Regional Studies"},{"key":"10.1016\/j.iswa.2026.200666_bib0055","doi-asserted-by":"crossref","first-page":"7791","DOI":"10.3390\/s23187791","article-title":"Spectrum sensing method based on residual dense network and attention","volume":"23","author":"Wang","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.iswa.2026.200666_bib0056","doi-asserted-by":"crossref","first-page":"917","DOI":"10.5194\/hess-28-917-2024","article-title":"A comprehensive study of deep learning for soil moisture prediction","volume":"28","author":"Wang","year":"2024","journal-title":"Hydrology and Earth System Sciences"},{"key":"10.1016\/j.iswa.2026.200666_bib0057","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131336","article-title":"Estimating soil moisture content in citrus orchards using multi-temporal sentinel-1A data-based LSTM and PSO-LSTM models","volume":"637","author":"Wu","year":"2024","journal-title":"Journal of Hydrology"},{"key":"10.1016\/j.iswa.2026.200666_bib0058","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.3390\/bioengineering12111268","article-title":"HCTG-net: A hybrid CNN\u2013Transformer network with gated fusion for automatic ECG arrhythmia diagnosis","volume":"12","author":"Xiong","year":"2025","journal-title":"Bioengineering"},{"key":"10.1016\/j.iswa.2026.200666_bib0059","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.3390\/w13081031","article-title":"FM-GRU: A time series prediction method for water quality based on seq2seq framework","volume":"13","author":"Xu","year":"2021","journal-title":"Water"},{"key":"10.1016\/j.iswa.2026.200666_bib0060","article-title":"A PatchTST-GRU based heterogeneous seq2seq model with numerical weather prediction refinement for multi-step wind power forecasting","volume":"15","author":"Xu","year":"2025","journal-title":"Scientific Reports"},{"key":"10.1016\/j.iswa.2026.200666_bib0061","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2020.106649","article-title":"A hybrid CNN-GRU model for predicting soil moisture in maize root zone","volume":"245","author":"Yu","year":"2021","journal-title":"Agricultural Water Management"},{"key":"10.1016\/j.iswa.2026.200666_bib0062","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/7167821","article-title":"Bearing fault diagnosis of end-to-end model design based on 1DCNN-GRU network","author":"Zhiwei","year":"2022","journal-title":"Discrete Dynamics in Nature and Society"},{"key":"10.1016\/j.iswa.2026.200666_bib0063","doi-asserted-by":"crossref","DOI":"10.1080\/10106049.2024.2441382","article-title":"A comparative analysis of deep learning models for accurate spatio-temporal soil moisture prediction","volume":"40","author":"Zhu","year":"2025","journal-title":"Geocarto International"},{"key":"10.1016\/j.iswa.2026.200666_bib0064","doi-asserted-by":"crossref","DOI":"10.1016\/j.iswa.2025.200532","article-title":"Metaheuristics in automated machine learning: Strategies for optimization","volume":"26","author":"Zito","year":"2025","journal-title":"Intelligent Systems with Applications"},{"key":"10.1016\/j.iswa.2026.200666_bib0065","doi-asserted-by":"crossref","unstructured":"Zrigui, I., Khoulji, S., Kerkeb, M.L., 2025. Integrated strategy for Urban traffic optimization: Prediction, adaptive signal control, and distributed communication via messaging. https:\/\/doi.org\/10.48550\/arXiv.2501.02008.","DOI":"10.20944\/preprints202412.2253.v1"}],"container-title":["Intelligent Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667305326000414?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667305326000414?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T11:29:53Z","timestamp":1778930993000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2667305326000414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":65,"alternative-id":["S2667305326000414"],"URL":"https:\/\/doi.org\/10.1016\/j.iswa.2026.200666","relation":{},"ISSN":["2667-3053"],"issn-type":[{"value":"2667-3053","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A novel hyper-tuned hybrid deep learning architecture for extended forecasting horizons: Application for soil moisture","name":"articletitle","label":"Article Title"},{"value":"Intelligent Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.iswa.2026.200666","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"200666"}}