Water Level Prediction of Lake Cascade Mahakam Using Adaptive Neural Network Backpropagation (ANNBP)

1st International Conference on Tropical Studies and Its Application

Mislan^1 ; Gaffar, A.F.O.^2 ; Haviluddin^3 ; Puspitasari, N.^3

Department of Physics, Faculty of Mathematics and Natural Science, Mulawarman University, Indonesia^1

Department of Information Technology, State of Polytechnic Samarinda, Indonesia^2

Department of Computer Science, Faculty of Computer Science and Information Technology, Mulawarman University, Indonesia^3

关键词: Adaptive neural networks;    Computational intelligence methods;    Flood event;    Lake water level;    Mean absolute percentage error;    Mean Square Error (MSE);    Natural hazard;    Water level prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/144/1/012009/pdf
DOI  :  10.1088/1755-1315/144/1/012009

来源: IOP

【 摘 要 】

A natural hazard information and flood events are indispensable as a form of prevention and improvement. One of the causes is flooding in the areas around the lake. Therefore, forecasting the surface of Lake water level to anticipate flooding is required. The purpose of this paper is implemented computational intelligence method namely Adaptive Neural Network Backpropagation (ANNBP) to forecasting the Lake Cascade Mahakam. Based on experiment, performance of ANNBP indicated that Lake water level prediction have been accurate by using mean square error (MSE) and mean absolute percentage error (MAPE). In other words, computational intelligence method can produce good accuracy. A hybrid and optimization of computational intelligence are focus in the future work.