Open AccessPrediction of Sediment Yield Using the Algoritma Lavenberg-Marquardt
Author(s) -
Uca,
Muhammad Ansarullah S. Tabbu,
Andi Makkawaru
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2123/1/012037
Subject(s) - sediment , erosion , mean squared error , hydrology (agriculture) , environmental science , yield (engineering) , structural basin , water quality , artificial neural network , coefficient of determination , soil science , statistics , geology , mathematics , computer science , geotechnical engineering , geomorphology , artificial intelligence , ecology , materials science , metallurgy , biology
Erosion and sediment that occurs in the basin is very important to be studied scientifically.Forcasting of sediment yield in a basins area is important to used to evaluate the land-use/landcover change, soil erosion hazard, planning, water quality, water resources in river, and to determine the extent of the damage that occurred in the basins. The algoritmh lavenberg-marquardt can be used to forcest the total of sediment yield the basin area. Artificial neural networks using feedforward multilayer percePsron with three learning algorithms namely Levenberg-Marquardt. The number of neurons of the hidden layer is three to sixteen, while in the output layer only one neuron because only one output target. The root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2 ), and coefficient of efficiency (CE). The performance value in the training process, R2, and CE (0.98 and 0.98). As well as for the testing process, R2 and CE (0.98 and 0.97). Based on the performance statistics value, LM is very suitable and accurate for to forcesting by modeling the non-linear complex behavior of sediment yield responses to water discharge, intensity of rainfall, and water depth in the river.