인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Two fundamental questions in rainfall modeling using Artificial Neural Networks (ANN) are: (1) how to best handle highly nonlinear, noisy, short-duration rainfall data, and (2) how to locate a solution to the optimization issue of ANN, particularly in formulating the optimum weights and biases. Most of the single-step rainfall forecasting models that rely on single-step optimization and conventional ANN feature selection techniques are not capable of adequately addressing these critical challenges. In this study, Non-Negative Matrix Factorization (NMF) is applied to model complex short-term rainfall patterns, and an optimal differential approach is used to compute NMF components. For the optimization of the ANN model, in this paper, three recently proposed metaheuristic algorithms namely Harris Hawks Optimization (HHO), Egret Swarm Optimization Algorithm (ESOA), and Hippopotamus Optimization (HO) are explored along with traditional well-known methods like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Furthermore, a two-step hybrid optimization approach is introduced to enhance ANN performance in rainfall forecasting. This significantly improves model precision, with reductions in mean squared error (MSE) ranging from 1.00% to 97.46% in Sylhet, 8.33% to 97.10% in Chittagong, 6.96% to 89.57% in Meghalaya, and 17.74% to 76.09% in Assam compared to single-step optimization models. The results indicate that the two-step optimization approach not only outperforms single optimization techniques but also outperforms recently introduced metaheuristic algorithms such as HHO, ESOA, and HO, demonstrating its superior potential for improving the accuracy of rainfall modeling. On the other hand, the sensitivity analysis of the hidden neuron indicates that the impact of hidden neuron quantity is highly sensitive to the optimizer and the characteristics of the data.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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