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.

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Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.

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