| Neural network modeling of hydrological systems: A review of implementation techniques O Oyebode, D Stretch Natural Resource Modeling 32 (1), e12189, 2019 | 89 | 2019 |
| Assessing the use of hybrid renewable energy system with battery storage for power generation in a University in Nigeria O Babatunde, I Denwigwe, O Oyebode, D Ighravwe, A Ohiaeri, ... Environmental Science and Pollution Research 29 (3), 4291-4310, 2022 | 38 | 2022 |
| River flow forecasting using an improved artificial neural network J Adeyemo, O Oyebode, D Stretch EVOLVE-A Bridge Between Probability, Set Oriented Numerics, and Evolutionary …, 2018 | 30 | 2018 |
| Urban water demand forecasting: a comparative evaluation of conventional and soft computing techniques O Oyebode, DE Ighravwe Resources 8 (3), 156, 2019 | 29 | 2019 |
| Review of three data-driven modelling techniques for hydrological modelling and forecasting OK Oyebode, FAO Otieno, J Adeyemo Fresenius environmental bulletin, 2014 | 25 | 2014 |
| Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals O Oyebode, DE Babatunde, CG Monyei, OM Babatunde Heliyon 5 (11), 2019 | 21 | 2019 |
| Evolutionary modelling of municipal water demand with multiple feature selection techniques O Oyebode Journal of Water Supply: Research and Technology—AQUA 68 (4), 264-281, 2019 | 19 | 2019 |
| Prediction of global warming potential and carbon tax of a natural gas-fired plant DE Babatunde, AN Anozie, JA Omoleye, O Oyebode, OM Babatunde, ... Energy Reports 6, 1061-1070, 2020 | 17 | 2020 |
| Design of a household biogas digester using co-digested cassava, vegetable and fruit waste N Sawyerr, C Trois, TS Workneh, O Oyebode, OM Babatunde Energy Reports 6, 1476-1482, 2020 | 16 | 2020 |
| Genetic programming: principles, applications and opportunities for hydrological modelling OK Oyebode, JA Adeyemo World Academy of Science, Engineering and Technology International Journal …, 2014 | 16 | 2014 |
| Monthly streamflow prediction with limited hydro-climatic variables in the Upper Mkomazi River, South Africa using genetic programming. O Oyebode, J Adeyemo, F Otieno | 11 | 2014 |
| Reservoir inflow forecasting using differential evolution trained neural networks O Oyebode, J Adeyemo EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary …, 2014 | 9 | 2014 |
| Comparison of two data-driven modelling techniques for long-term streamflow prediction using limited datasets OK Oyebode, JA Adeyemo, FAO Otieno Journal of the South African Institution of Civil Engineering 57 (3), 9-19, 2015 | 7 | 2015 |
| Modelling streamflow response to hydro-climatic variables in the Upper Mkomazi River, South Africa OK Oyebode | 6 | 2014 |
| Uncertainty sources in climate change impact modelling of water resource systems OK Oyebode, J Adeyemo, FAO Otieno Academic journal of science (CD-ROM), 2014 | 4 | 2014 |
| Modelling the power output from a steam power plant in Nigeria DE Babatunde, AN Anozie, JA Omoleye, O Oyebode, OM Babatunde, ... Energy Reports 6, 822-828, 2020 | 3 | 2020 |
| Development of a sustainable evolutionary-inspired artificial intelligent system for municipal water demand modelling. OK Oyebode | 1 | 2020 |
| Denitrification of leachate using composted domestic waste at different levels of stability: A batch test investigation N Sawyerr, C Trois, O Oyebode, JK Bwapwa Scientific African 14, e00989, 2021 | | 2021 |
| Simulating Effects of Drainage Design Parameters on Optimum Crop Yield Using DRAINMOD TA Ewemoje, OK Oyebode, OA Akintola, OE Ewemoje TMDL 2010: Watershed Management to Improve Water Quality Proceedings, 14-17 …, 2010 | | 2010 |
Olubayo Moses BabatundeUniversity of Lagos在 unilag.edu.ng 的电子邮件经过验证
