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
The main goal of this study is to predict nitrate (NO 3- ) levels in the Saiss basin water table as a function of various physicochemical parameters. To accomplish this, three machine learning approaches were utilized: multiple linear regression (MLR), super vector regression (SVR), and artificial neural networks (ANN). The independent variables were composed of six water quality parameters, including Ca 2+ , Na 2+ , EC, Cl - , HCO 3- , and SO 4 2- . The study utilized a dataset of 389 water samples collected between 1991 and 2017. The artificial neural network (ANN) was trained using the Levenberg-Marquardt (LM) algorithm, which was selected from various optimization algorithms. Additionally, during the training of the SVR model, it was observed that the RBF kernel outperformed the other kernels (linear, polynomial, and sigmoid kernel). The results were analyzed by the coefficient of determination (R2) and the mean square error (MSE). The results of the MLR method revealed R2 (0.523) and MSE (757.34). The ANN model with architecture [6-20-1] performed better than RLM with R2 = 0.836, MSE= 0.023 The SVR model result confirms what has been proved by ANN concerning the performance, with R2=0.902 and MSE= 4,364.
#Artificial neural network
#Mean squared error
#Sigmoid function
#Support vector machine
#Coefficient of determination
#Linear regression
#Mathematics
#Kernel (algebra)
#Radial basis function
#Radial basis function kernel
#Artificial intelligence
#Machine learning
#Statistics
#Computer science
#Kernel method
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