@article{fdi:010092155,
  title = {{P}redicting climate-driven changes in reservoir inflows and hydropower in {C}{\^o}te d'{I}voire using machine learning modeling},
  author = {{O}bahoundje, {S}. and {D}iedhiou, {A}rona and {A}kpoti, {K}. and {K}ouassi, {K}. {L}. and {O}fosu, {E}. {A}. and {K}ouame, {D}. {G}. {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his study investigates the impact of climate change and variability on reservoir inflow and hydropower generation at three key hydropower plants in {C}{\^o}te d'{I}voire including {B}uyo, {K}ossou, and {T}aboo. {T}o simulate inflow to reservoir and energy generation, the {R}andom {F}orest ({RF}), a machine-learning algorithm allowing fewer input variables was applied. {I}n three-step, {RF} k-fold cross validation (with k = 5) was used; (i) 12 and 6 multiple lags of precipitation and temperature at monthly increments were used as predictors, respectively; (ii) the five most important variables were used in addition to the current month's precipitation and temperature; and (iii) a residual {RF} was built. {T}he bias-adjusted ensemble mean of eleven climate models output of the {CO}ordinated {R}egional {D}ownscaling {E}xperiment was used for the representative concentration pathways ({RCP}4.5 and {RCP}8.5). {T}he model output was highly correlated with the observations, with {P}earson correlations >0.90 for inflow and >0.85 for energy for the three hydropower plants. {T}he temperature in the selected sub-catchments may increase significantly from 0.9 to 3 degrees {C} in the near (2040-2069) and from 1.7 to 4.2 degrees {C} in far (2070-2099) future periods relative to the reference period (1981-2010). {A} time series of precipitation showed a change in range -7 and 15 % in the near and -8 to 20 % in the far future and more years are with increasing change. {D}epending on the sub-catchment, the magnitude of temperature and precipitation changes will increase as greenhouse gas emissions ({GHG})(greater in {RCP}8.5 than {RCP}4.5) rise. {A}t all time scales (monthly, seasonal, and annual), the simulated inflow and energy changes were related to climate variables such as temperature and precipitation. {A}t the annual time scale, the inflow is projected to change between -10 and 37 % and variability may depend on the reservoir. {H}owever, the energy change is promised to change between -10 and 25 %, -30 to 15 %, and 5-40 % relative to the historical (1981-2010) period for {T}aabo, {K}ossou, and {B}uyo dams, respectively at an annual scale. {T}he changes may vary according to the year, the {RCP}s, and the dam. {C}onsequently, decision-makers are recommended to take into consideration an energy mix plan to meet the energy demand in these seasons.},
  keywords = {{T}ime series change ; {C}limate variability and change ; {H}ydropower generation ; {M}achine learning ; {R}andom forest ; {C}{\^o}te d'{I}voire ; {COTE} {D}'{IVOIRE}},
  booktitle = {},
  journal = {{E}nergy},
  volume = {302},
  numero = {},
  pages = {131849 [14 p.]},
  ISSN = {0360-5442},
  year = {2024},
  DOI = {10.1016/j.energy.2024.131849},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092155},
}