Abstract Groundwater is often one of the significant natural sources freshwater supply, especially in arid and semi-arid regions, paramount importance. This study provides a new high accurate technique for forecasting groundwater level (GWL). The artificial intelligence (AI) models include neural network (ANN) multi-layer perceptron (MLP) radial basis function (RBF), adaptive neural-fuzzy infer...

Alternative forms of neural networks have been applied to forecast daily river flows on a continuous basis with the purpose of understanding how recent architectures like ANFIS, GRNN and RBF compare with traditional FFBP when monsoon-fed rivers involving significant statistical bias are involved. The forecasts are made at a location called Rajghat along river Narmada in India. Division of yearl...

Evaporation as a major meteorological component of the hydrologic cycle plays a key role in water resources studies and climate change. The estimation of evaporation is a complex and unsteady process, so it is difficult to derive an accurate physical-based formula to represent all parameters that effect on estimate evaporation. Artificial intelligence-based methods may provide reliable predicti...

In this paper performance of Puma 560 manipulator is being compared for hybrid gradient descent and least square method learning based ANFIS controller with hybrid Genetic Algorithm and Generalized Pattern Search tuned radial basis function based Neuro-Fuzzy controller. ANFIS which is based on Takagi Sugeno type Fuzzy controller needs prior knowledge of rule base while in radial basis function ...

Rapid growth of machine learning methodologies and their applications offer new opportunity for improved transformer asset management. Accordingly, power system operators are currently looking for data-driven methods to make better-informed decisions in terms of network management. In this paper, machine learning and data fusion techniques are integrated to estimate transformer loss of life. Us...

The purpose of this study is to investigate the accuracy of predictions of aniline removal efficiency in a moving bed biofilm reactor (MBBR) by various methods, namely by RBF, ANFIS, and fuzzy regression analysis. The reactor was operated in an aerobic batch and was filled by light expanded clay aggregate (LECA) as a carrier for the treatment of Aniline synthetic wastewater. Exploratory data an...

the purpose of this study is to investigate the accuracy of predictions of aniline removal efficiency in a moving bed biofilm reactor (mbbr) by various methods, namely by rbf, anfis, and fuzzy regression analysis. the reactor was operated in an aerobic batch and was filled by light expanded clay aggregate (leca) as a carrier for the treatment of aniline synthetic wastewater. exploratory data an...

In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use ...