Data pre-processing effect on ANN-based prediction intervals construction of the evaporation process at different climate regions in Iran

Nourani, Vahid; Sayyah-Fard, M.; Alami, Mohammad Taghi; Sharghi, Elnaz

doi:10.1016/j.jhydrol.2020.125078

Cited by 30 publications

(10 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was indicated that the LUBE method could be successfully used to compute the PIs of ANN-based downscaling with reliable performance. Nourani, Sayyah-Fard, Alami and Sharghi [65] quantified the uncertainty of the ANN-based evaporation modeling via the LUBE method. It was claimed that the LUBE method could construct PIs with an appropriate level of reliability; however, data pre-processing methods could affect the uncertainty.…”

Section: Lube Methodsmentioning

confidence: 99%

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

2021

Self Cite

View full text Add to dashboard Cite

Despite the wide applications of artificial neural networks (ANNs) in modeling hydro-climatic processes, quantification of the ANNs’ performance is a significant matter. Sustainable management of water resources requires information about the amount of uncertainty involved in the modeling results, which is a guide for proper decision making. Therefore, in recent years, uncertainty analysis of ANN modeling has attracted noticeable attention. Prediction intervals (PIs) are one of the prevalent tools for uncertainty quantification. This review paper has focused on the different techniques of PI development in the field of hydrology and climatology modeling. The implementation of each method was discussed, and their pros and cons were investigated. In addition, some suggestions are provided for future studies. This review paper was prepared via PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology.

show abstract

Section: Lube Methodsmentioning

confidence: 99%

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…To verify the superiority of the combined model over the component neural networks (SVM, BP, WAVENN, and ELM), seven indexes, namely, MAPE [41], PICP [42], MPIW [43], CWC [44], ACE [45], MPICD [46], and NMPICD (min-max) [36] are selected for the comprehensive evaluation.…”

Section: Experiments Imentioning

confidence: 99%

A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

Tian

Wang

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

As a clean energy source, the role of wind power in the energy mix is becoming increasingly important. Reliable and high-quality wind speed prediction results are key to wind energy utilization. The traditional point prediction method cannot effectively analyze the uncertainty of wind speed, and the interval prediction model can provide the possible variation range of wind speed under a certain confidence probability and supply more uncertain information to decision makers. However, the previous interval prediction models generally ignore the random characteristics of capturing wind speed and the importance of objective selection of prediction submodels, leading to poor prediction results. To address these problems, a combined model based on data preprocessing, multi-neural network models, multi-objective optimization, and an improved interval prediction method is proposed. The model is applied to five wind speed forecasting examples in Dalian to test the prediction accuracy, multi-step prediction ability, and universality and generalization ability of the model. The experimental results show that the model proposed in this study is satisfactory for various performance evaluation indexes, has high stability and accuracy, and all the solutions obtained by the model are Pareto optimal solutions. Thus, it provides a reliable reference for the effective utilization of wind energy.

show abstract

“…Precise measurement of some of these meteorological factors requires advanced tools and skilled labor [17]. Often, instrument malfunctions, improper maintenance, and harsh weather conditions make it difficult to gauge these data minus any errors, which is essential for the prediction of evaporation via empirical equations [18]. us, it would be problematic to project evaporation by gauging these factors incorrectly [19].…”

Section: Introductionmentioning

confidence: 99%

Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study

Sudani

Salem

2022

Advances in Meteorology

View full text Add to dashboard Cite

Accurately estimating the amount of evaporation loss is necessary for scheduling and calculating irrigation water requirements. In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian process regression (GPR), have been developed to estimate the monthly evaporation loss over two stations located in Iraq. Monthly climatical parameters have been used as an input variable for simulating the evaporation rate. Several statistical measures (e.g., mean absolute error (MAE), correlation coefficient (R), mean absolute percentage error (MAPE), and modified index of agreement (Md)), as well as graphical inspection, were used to compare the performances of the applied models. The results showed that the GBM model has much better performance in predicting monthly evaporation over two stations compared to other applied models. For the first case study which was in Diyala, the results showed a prediction enhancement in terms of MAE and RMSE by 7.17%, 21.01%; 16.51%, 15.74%; and 23.14%, 26.64%; using GBM compared to ELM, GPR, and QRF, respectively. However, for the second case study (in Erbil), the prediction enhancement was improved in terms of reduction of MAE and RMSE by 10.88%, 9.24%; 15.24%, 5%; and 16.06%, 15.76%; respectively, compared to ELM, GPR, and QRF models. The results of the proposed GMBM model can therefore assist local stakeholders in the management of water resources.

show abstract

Data pre-processing effect on ANN-based prediction intervals construction of the evaporation process at different climate regions in Iran

Cited by 30 publications

References 44 publications

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study

Contact Info

Product

Resources

About