Klang River–Level Forecasting Using ARIMA and ANFIS Models

Selection of the right modeling technique is always a challenging issue because every model can produce only an approximation of the reality it is attempting to illustrate. As a result, model performance in a specific situation is the only criterion that confirms the model's applicability in that particular situation. This study investigated the applicability of the adaptive neuro‐fuzzy inference system (ANFIS) and the autoregressive integrated moving average (ARIMA) models in water‐level modeling. Results showed a definite preference for the ANFIS model against the simple‐ARIMA model, but an updated‐ARIMA model outperformed ANFIS. A mean absolute error of < 1% in each model confirmed the applicability of these models in predicting the water level in the Klang River in Malaysia. On the basis of the obtained prediction accuracy level, the updated‐ARIMA and ANFIS models are introduced as reliable and accurate models for prompt decision‐making, planning, and urgent managing of water resources in crisis.