Elshorbagy and Parasuraman (2008) investigated the utility of the widely adopted data-driven model namely artificial neural networks (ANNs) for modelling the complex soil moisture dynamics Datasets from three experimental soil covers (D1 D2 and D3) with thickness of 0 50 m 0 35 m and 1 0 m comprising a thin layer of peat mineral mix different moisture contents on four various surfaces They found that increasing moisture content of seeds resulted in the increment of the SFC Dursun et al (2007) measured the SFC of sugarbeet seed on rubber wood galvanized steel and aluminum surfaces

Modeling of Soil Water Content for Vegetated Surface by

2018-2-13Comparisons showed that ANFIS approach had a better modeling potential of the soil water content compared to the MLR and ANN model in the trial period though weaker relationships in the testing period were found by all approaches Keywords: Soil Water Content Adaptive Neuro-Fuzzy Inference System Artificial Neural Network

promising prediction methods for both long-term and short-term floods Furthermore the major trends in improving the quality of the flood prediction models are investigated Among them hybridization data decomposition algorithm ensemble and model optimization are reported as the most effective strategies for the improvement of ML methods

In another research work by Bisht et al (2009) the groundwater water table assessment and prediction was done by the models of FL and ANFIS and compared to ascertain which model is better in groundwater process analysis in Budaun district in Inida and with the two models performing very well however the ANFIS model proved to give better

2019-2-27ing the density of the soil soil moisture special weight dirt and the swelling Materialsandmethods Case study region To verify the accuracy and applicability of the proposed linear model a case study was carried out based on requirements of the project in a farmland at Karaj Iran ˝e farm area was 70˛ha and was located in west of Karaj

The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management In this study we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates

Prediction of Methyl Salicylate Effects on Pomegranate

2020-6-17The overall agreement between ANFIS predictions and experimental data was also significant (r=0 87) In addition sensitivity analysis results showed that storage time was the most sensitive factor for prediction of MeSA effects on pomegranate fruit

The SSA-ANN2 model with runoff rainfall and soil moisture as inputs and using data preprocessing technique performed the best The degree of improvement by SSA was more significant than by the inclusion of soil moisture Thus the ANN model coupled with SSA is more promising for runoff forecast

Results of ANFIS model prediction In this section the results of ANFIS models for prediction of LE FE TMC and TME are presented MATLAB programming language was used for implementing ANFIS simulations Different ANFIS structures were tried using the programming code and the appropriate representations were determined

2015-12-9A multivariate conditional model for streamflow prediction and spatial precipitation refinement Zhiyong Liu1 Ping Zhou2 Xiuzhi Chen3 and Yinghui Guan4 5 1Institute of Geography Heidelberg University Heidelberg Germany 2Department of Forest Ecology Guangdong AcademyofForestry Guangzhou China 3SouthChinaBotanicalGarden ChineseAcademyofSciences Guangzhou China

2018-2-16that models the data behaviour The main computation ANFIS procedure includes four steps: (1) data input (2) assigning fuzzy sets (3) using ANFIS training function in the toolbox for the training of the input data and (4) output prediction After training an ANFIS model is obtained for output prediction In this study a genfis2 model in

2013-7-5Artificial Neural Networks (ANNs) simulation of brain actions to exploit the massive parallel local processes ANNs can learn from the facts or input data and the associated output data After developing ANNs model it can accept an input and present corresponding output In ANNs BP network models are common to engineer

In this paper the authors develop an ensemble CI-statistical technique to model moisture damage in lime- and chemically modified asphalt In this process first a number of well-known CI techniques are applied namely Artificial Neural Networks (ANNs) Support Vector Regression (SVR) and an Adaptive Neuro Fuzzy Inference System (ANFIS)

The SSA-ANN2 model with runoff rainfall and soil moisture as inputs and using data preprocessing technique performed the best The degree of improvement by SSA was more significant than by the inclusion of soil moisture Thus the ANN model coupled with SSA is more promising for runoff forecast

Predictionofenvironmentalindicators

2019-2-27ing the density of the soil soil moisture special weight dirt and the swelling Materialsandmethods Case study region To verify the accuracy and applicability of the proposed linear model a case study was carried out based on requirements of the project in a farmland at Karaj Iran ˝e farm area was 70˛ha and was located in west of Karaj

promising prediction methods for both long-term and short-term floods Furthermore the major trends in improving the quality of the flood prediction models are investigated Among them hybridization data decomposition algorithm ensemble and model optimization are reported as the most effective strategies for the improvement of ML methods

Parameter ANFIS Non-linear regression ANN When the VAF and RMSE performance parameters are FCM SCM considered for each predictive model (Table 6) it can be R 0 9823 0 5648 0 93 0 9745 clearly seen that the developed ANFIS model with FCM method gives a better prediction performance than that of RMSE 10 46 157 09 18 35 12 34 the ANFIS with SCM

The ANNs worked well especially where the dataset was not complete providing a viable choice for accurate prediction ANNs provided the possibility of reducing the analytical costs through reducing the data analysis time that used to face in e g [198] Similarly reference [87] used ANNs to develop a prediction model for precipitation

To investigate the effectiveness of the proposed model an IDM improved by a genetic algorithm (GIDM) for runoff prediction and interpolation experiments divided into two groups have been made An application of GIDM for monthly discharge reconstructing and forecasting is compared with SIDM and OIDM using the same sparse observed data at

2020-4-14The model's input selection is a necessary step to assure the successfulness of the model performance achieving accurate prediction accuracy for the model's output However the existing research manuscripts for ST prediction did pay attention for this step as long as the required data are available for the model developers

ANFIS method had the higher ability to evaluate all output (moisture ratio drying rate D eff and SEC) as compared to ANNs method Abstract The main purpose of this study was to develop and apply an adaptive neuro-fuzzy inference system (ANFIS) and Artificial Neural Networks (ANNs) model for predicting the drying characteristics of potato