there is different methods for simulating river flow. some of thesemethods such as the process based hydrological models need multiple input data and high expertise about the hydrologic process. but some of the methods such as the regression based and artificial inteligens modelsare applicable even in data scarce conditions. this capability can improve efficiency of the hydrologic modeling in u...

Bedrock unconfined compressive strength (UCS) is a key parameter in designing thegeosciences and building related projects comprising both the underground and surface rock structures. Determination of rock UCS using standard laboratory tests is a complicated, expensive, and time-consuming process, which requires fresh core specimens. However, preparing fresh cores is not always possible, especi...

Fuzzy systems (FSs) are popular and interpretable machine learning methods, represented by the adaptive neuro-fuzzy inference system (ANFIS). However, they have difficulty dealing with high-dimensional data due to curse of dimensionality. To effectively handle ensure optimal performance, this paper presents a deep neural fuzzy (DNFS) based on subtractive clustering-based ANFIS (SC-ANFIS). Inspi...

Cement is among the major contributors to global carbon dioxide emissions. Thus, sustainable alternatives conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics be a option further research and investigation. Since experimental work required assessing its properties both time consuming complex, machine learning can used suc...

Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. This paper explores evaporation estimation methods based o...

This paper presents novel GA-ANFIS expert system prototype for dermatological disease detection by using dermatological features and diagnoses collected in real conditions. Nine dermatological features are used as inputs to classifiers that are based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. After that, they are used as inputs in Genetic ...

Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rulebase optimization methods to perform efficiently whe...

This paper presents a PID tuning method for unstable processes using an adaptive-network-based-fuzzy-inference system (ANFIS) for given gain and phase margin (GPM) speci)cations. PID tuning methods are widely used to control stable processes. However, PID controller for unstable processes is less common. In this paper, the PID controller parameters can be determined by the ANFIS. Because the de...