摘要
It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.
It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.
作者
Joseph Armathé Amougou
Isidore Séraphin Ngongo
Patrick Forghab Mbomba
Romain Armand Soleil Batha
Paul Ghislain Poum Bimbar
Joseph Armathé Amougou;Isidore Séraphin Ngongo;Patrick Forghab Mbomba;Romain Armand Soleil Batha;Paul Ghislain Poum Bimbar(Dpartement de Gographie, Universit de Yaound 1, Yaound, Cameroon;Observatoire National sur les Changements Climatiques (ONACC), Yaound, Cameroon;Laboratoire dAnalyse et Modlisation Multidisciplinaire Statistique (SAMM), Universit de Paris 1 Panthon Sorbonne, Paris, France;Ecole Nationale Suprieure Polytechnique de Yaound 1, Yaound, Cameroon;Dpartement de Mathmatiques, Ecole Normale Suprieure de Yaound, Yaound, Cameroon;Dpartement dInformatique, Universit de Yaound 1, Yaound, Cameroon)
