  Estimation of the wind speed plays an important role in many issues such as
route determination of ships, efficient use of wind roses, and correct planning
of agricultural activities. In this study, wind velocity estimation is
calculated using artificial neural networks (ANN) and adaptive artificial
neural fuzzy inference system (ANFIS) methods. The data required for estimation
was obtained from the float named E1M3A, which is a float inside the POSEIDON
float system. The proposed ANN is a Nonlinear Auto Regressive with External
Input (NARX) type of artificial neural network with 3 layers, 50 neurons, 6
inputs and 1 output. The ANFIS system introduced is a fuzzy inference system
with 6 inputs, 1 output, and 3 membership functions (MF) per input. The
proposed systems were trained to make wind speed estimates after 3 hours and
the data obtained were obtained and the successes of the systems were revealed
by comparing the obtained values with real measurements. Mean Squarred Error
(MSE) and the regression between the predictions and expected values (R) were
used to evaluate the success of the estimation values obtained from the
systems. According to estimation results, ANN achieved 2.19 MSE and 0.897 R
values in training, 2.88 MSE and 0.866 R values in validation, and 2.93 MSE and
0.857 R values in testing. ANFIS method has obtained 0.31634 MSE and 0.99 R
values
