Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS

Goyal, Manish Kumar; Bharti, Birendra; Quilty, John; Adamowski, Jan; Pandey, Ashish

doi:10.1016/j.eswa.2014.02.047

Cited by 266 publications

(105 citation statements)

References 60 publications

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“…The total sunshine duration and precipitation variability were negatively correlated with vegetation coverage. Long hours of sunshine led to a high evaporation capacity, or potential evaporation (Goyal, Bharti, Quilty, Adamowski, & Pandey, 2014), and increased water stress. This stress drove xerophytes to become the dominant plants in the community, potentially decreasing the total vegetation coverage.…”

Section: Effects Of Environmental Factors On Vegetation Recoverymentioning

confidence: 99%

The vegetation recovery pattern and affecting factors after pipeline disturbance in northwest China

Xiao

Shi

Wang

et al. 2016

Journal for Nature Conservation

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a b s t r a c tPipeline construction has severe disturbance on local ecosystems, especially in arid and semi-arid regions. Vegetation recovery along the pipeline is crucial for ecosystem management and eco-environment restoration. In this study, three typical regions with different backgrounds and engineering characteristics in northwest China were selected to analyze vegetation recovery pattern after pipeline disturbance by using comparative analysis and RDA analysis. The results showed that vegetation condition in the zone 10 m from the pipeline was worse than construction area above the pipeline, and was identified as a key area in which vegetation restoration needs to occur. Rainfall and total sunshine duration were found to be key factors affecting vegetation recovery. Additionally, recovery duration also played important roles in vegetation recovery. The affecting factors can be divided into two categories: environmental factors; and, engineering factors. Environmental factors contribute more to the differences of vegetation in different regions, while the engineering factors may play a more important role in relation to vegetation recovery within a region. In general, the effects of pipeline construction on vegetation pattern were confined to 300 m from the pipeline based on this study. However, artificial measures may hasten the vegetation recovery process. This research could provide scientific guidance for ecological restoration and environmental management for the construction of linear projects.

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Section: Effects Of Environmental Factors On Vegetation Recoverymentioning

confidence: 99%

The vegetation recovery pattern and affecting factors after pipeline disturbance in northwest China

Xiao

Shi

Wang

et al. 2016

Journal for Nature Conservation

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“…This is achieved through the application of a learning algorithm using input-output data sets. The optimisation of parameters during the training session is undertaken in such a way that the error between the target and actual output is minimized (Goyal et al 2014). The parameters to be optimized in ANFIS are the premise parameters, which describe the shape of the membership functions, and the consequent parameters, which describe the overall output of the ANFIS system.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

2015

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Abstract. This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia's domestic low cost carriers' demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model's training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R 2 -value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

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“…Another recent study proposes the coupling of the kernel partial least squares-autoregressive moving average with wavelet transformation as a hybrid approach for modeling annual urban water demand [22]. On the other hand, support vector regression/machine (SVR/SVM)-based models have become increasingly popular recently [23][24][25][26]. Other data-driven techniques, which are not that common, are random forests and multivariate adaptive regression splines [24].…”

Section: Introductionmentioning

confidence: 99%

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

Shabani

Candelieri

Archetti

et al. 2018

Water

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This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand data of the city of Milan, in Italy. Moreover, multi-scale modeling using a series of head-time was deployed to investigate the optimum temporal resolution under study. Multi-scale modeling was performed based on rearranging hourly based patterns of water demand into 3, 6, 12, and 24 h lead times. Results showed that GEP should receive more attention among the emerging nonlinear modelling techniques if coupled with unsupervised learning algorithms in detailed spherical k-means.

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Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS

Cited by 266 publications

References 60 publications

The vegetation recovery pattern and affecting factors after pipeline disturbance in northwest China

The vegetation recovery pattern and affecting factors after pipeline disturbance in northwest China

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts

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