a hidden‎ markov model‎ ‎based‎ ‎extended case-based reasoning algorithm for relief materials demand forecasting

‎in emergency situations‎, ‎accurate demand forecasting for relief materials such as food‎, ‎water‎, ‎and medicine is crucial for effective disaster response‎. ‎this research is presented a novel algorithm to demand forecasting for relief materials using extended case-based reasoning (cbr) with the best-worst method (bwm) and hidden markov models (hmms)‎. ‎the proposed algorithm involves training an hmm on historical data to obtain a set of state sequences representing the temporal fluctuations in demand for different relief materials‎. ‎when a new disaster occurs‎, ‎the algorithm first determines the current state sequence using the available data and searches the case library for past disasters with similar state sequences‎. ‎the effectiveness of the proposed algorithm is demonstrated through experiments on real-world disaster data of iran‎. ‎based on the results‎, ‎the forecasting error index for four relief materials is less than 10%; therefore‎, ‎the proposed cbr-bwm-hmm is a strong and robust algorithm‎.