Selection of CMIP5 GCM ensemble for the projection of spatio-temporal changes in precipitation and temperature over the Niger Delta, Nigeria

Selection of a suitable general circulation model (GCM) ensemble is crucial for effective water resource management and reliable climate studies in developing countries with constraint in human and computational resources. A careful selection of a GCM subset by excluding those with limited similarity to the observed climate from the existing pool of GCMs developed by different modeling centers at various resolutions can ease the task and minimize uncertainties. In this study, a feature selection method known as symmetrical uncertainty (SU) was employed to assess the performance of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM outputs under Representative Concentration Pathway (RCP) 4.5 and 8.5. The selection was made according to their capability to simulate observed daily precipitation (prcp), maximum and minimum temperature (Tmax and Tmin) over the historical period 1980–2005 in the Niger Delta region, which is highly vulnerable to extreme climate events. The ensemble of the four top-ranked GCMs, namely ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M, were selected for the spatio-temporal projection of prcp, Tmax, and Tmin over the study area. Results from the chosen ensemble predicted an increase in the mean annual prcp between the range of 0.26% to 3.57% under RCP4.5, and 0.7% to 4.94% under RCP 8.5 by the end of the century when compared to the base period. The study also revealed an increase in Tmax in the range of 0 to 0.4 °C under RCP4.5 and 1.25–1.79 °C under RCP8.5 during the periods 2070–2099. Tmin also revealed a significant increase of 0 to 0.52 °C under RCP4.5 and between 1.38–2.02 °C under RCP8.5, which shows that extreme events might threaten the Niger Delta due to climate change. Water resource managers in the region can use these findings for effective water resource planning, management, and adaptation measures

Прогнозування за допомогою імунних алгоритмів

Дана дипломна робота містить 111 с., 6 табл., 24 рис., 2 дод., 42 джерел. Тема: Прогнозування за допомогою імунних алгоритмів. У роботі розв’язується задача прогнозування часових рядів за допомогою імунних алгоритмів. Об’єкт дослідження: сучасні методи прогнозування часових рядів за допомогою імунних алгоритмів. Предмет дослідження: засоби моделювання і прогнозування з застосуванням імунних алгоритмів. Мета роботи: дослідити наявні імунні моделі для розв’язання задачі прогнозування часових рядів. Методи дослідження: використано математичний апарат імунних алгоритмів для прогнозування.This thesis contains 111 p., 6 tabl., 24 fig., 2 appendice, 42 sources. Theme: Forecasting using immune algorithms. The problem of predicting time series using immune algorithms is solved in the work. Object of research: modern methods of forecasting time series using immune algorithms. Subject of research: means of modeling and forecasting with the use of immune algorithms. Objective: To investigate existing immune models to solve the problem of time series prediction. Research methods: used mathematical apparatus of immune algorithms for prediction