These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
138 related articles for article (PubMed ID: 37318921)
21. Using SWAT to Evaluate Streamflow and Lake Sediment Loading in the Xinjiang River Basin with Limited Data. Yuan L; Forshay KJ Water (Basel); 2019 Dec; 12(1):39. PubMed ID: 32983578 [TBL] [Abstract][Full Text] [Related]
22. Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers. Wan W; Zhou Y; Chen Y Environ Sci Pollut Res Int; 2024 May; 31(23):34588-34606. PubMed ID: 38710844 [TBL] [Abstract][Full Text] [Related]
23. Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region. Hameed MM; Mohd Razali SF; Wan Mohtar WHM; Ahmad Alsaydalani MO; Yaseen ZM Heliyon; 2024 Jan; 10(1):e22942. PubMed ID: 38187234 [TBL] [Abstract][Full Text] [Related]
24. Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction. Demiray BZ; Sit M; Mermer O; Demir I Water Sci Technol; 2024 May; 89(9):2326-2341. PubMed ID: 38747952 [TBL] [Abstract][Full Text] [Related]
25. A method for detecting the non-stationarity during high flows under global change. Zhang Z; Huang J; Wagner PD; Fohrer N Sci Total Environ; 2022 Dec; 851(Pt 2):158341. PubMed ID: 36037886 [TBL] [Abstract][Full Text] [Related]
26. Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction. Van Thieu N; Nguyen NH; Sherif M; El-Shafie A; Ahmed AN Sci Rep; 2024 Jun; 14(1):13597. PubMed ID: 38866871 [TBL] [Abstract][Full Text] [Related]
27. Assessment of the total organic carbon employing the different nature-inspired approaches in the Nakdong River, South Korea. Kim S; Maleki N; Rezaie-Balf M; Singh VP; Alizamir M; Kim NW; Lee JT; Kisi O Environ Monit Assess; 2021 Jun; 193(7):445. PubMed ID: 34173069 [TBL] [Abstract][Full Text] [Related]
28. Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test. Vishwakarma DK; Kuriqi A; Abed SA; Kishore G; Al-Ansari N; Pandey K; Kumar P; Kushwaha NL; Jewel A Heliyon; 2023 May; 9(5):e16290. PubMed ID: 37251828 [TBL] [Abstract][Full Text] [Related]
29. Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea. Kwon Y; Cha Y; Park Y; Lee S Sci Rep; 2023 Jun; 13(1):9296. PubMed ID: 37291216 [TBL] [Abstract][Full Text] [Related]
30. An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Deo RC; Şahin M Environ Monit Assess; 2016 Feb; 188(2):90. PubMed ID: 26780409 [TBL] [Abstract][Full Text] [Related]
31. Projected Streamflow in the Kurau River Basin of Western Malaysia under Future Climate Scenarios. Adib MNM; Rowshon MK; Mojid MA; Habibu I Sci Rep; 2020 May; 10(1):8336. PubMed ID: 32433561 [TBL] [Abstract][Full Text] [Related]
32. Application of deep learning approaches to predict monthly stream flows. Dalkilic HY; Kumar D; Samui P; Dixon B; Yesilyurt SN; Katipoğlu OM Environ Monit Assess; 2023 May; 195(6):705. PubMed ID: 37212953 [TBL] [Abstract][Full Text] [Related]
33. Prediction of water quality parameters using machine learning models: a case study of the Karun River, Iran. Nouraki A; Alavi M; Golabi M; Albaji M Environ Sci Pollut Res Int; 2021 Oct; 28(40):57060-57072. PubMed ID: 34081285 [TBL] [Abstract][Full Text] [Related]
34. Identifying the impact of land use land cover change on streamflow and nitrate load following modeling approach: a case study in the upstream Dong Nai River basin, Vietnam. Le TH; Nguyen TNQ; Tran TXP; Nguyen HQ; Truong NCQ; Le TL; Pham VH; Pham TL; Tran THY; Tran TT Environ Sci Pollut Res Int; 2023 Jun; 30(26):68563-68576. PubMed ID: 37121945 [TBL] [Abstract][Full Text] [Related]
35. Predicting in-stream water quality constituents at the watershed scale using machine learning. Adedeji IC; Ahmadisharaf E; Sun Y J Contam Hydrol; 2022 Dec; 251():104078. PubMed ID: 36206579 [TBL] [Abstract][Full Text] [Related]
36. River suspended sediment modelling using the CART model: A comparative study of machine learning techniques. Choubin B; Darabi H; Rahmati O; Sajedi-Hosseini F; Kløve B Sci Total Environ; 2018 Feb; 615():272-281. PubMed ID: 28982076 [TBL] [Abstract][Full Text] [Related]
37. Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling. Heddam S; Ptak M; Sojka M; Kim S; Malik A; Kisi O; Zounemat-Kermani M Environ Sci Pollut Res Int; 2022 Oct; 29(47):71555-71582. PubMed ID: 35604598 [TBL] [Abstract][Full Text] [Related]
38. A comparative study of data-driven models for runoff, sediment, and nitrate forecasting. Zamani MG; Nikoo MR; Rastad D; Nematollahi B J Environ Manage; 2023 Sep; 341():118006. PubMed ID: 37163836 [TBL] [Abstract][Full Text] [Related]
39. Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso. Song Y; Zhang J Water Sci Technol; 2024 May; 89(9):2367-2383. PubMed ID: 38747954 [TBL] [Abstract][Full Text] [Related]
40. Integrating remotely sensed surface water extent into continental scale hydrology. Revilla-Romero B; Wanders N; Burek P; Salamon P; de Roo A J Hydrol (Amst); 2016 Dec; 543(Pt B):659-670. PubMed ID: 28111480 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]