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

Nourani, Vahid; Paknezhad, Nardin Jabbarian; Tanaka, Hitoshi

doi:10.3390/su13041633

Cited by 18 publications

(14 citation statements)

References 70 publications

(84 reference statements)

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“…For example, the bootstrap-based approach would create an ensemble of ANNs for the same WRRF treatment alternative, of which the computation time will depend on the number of ANN instances created within an ensemble. 44 3.3. Long-Term Simulation of Wastewater Treatment under Wet and Drought Weather.…”

Section: Resultsmentioning

confidence: 99%

Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling

Emaminejad

Aguiar

et al. 2021

ACS EST Eng.

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Integrated watershed modeling is needed to couple water resource recovery facilities (WRRFs) with agricultural management for holistic watershed nutrient management. Surrogate modeling can facilitate model coupling. This study applies artificial neural networks (ANNs) as surrogate models for WRRF models to efficiently evaluate the long-term treatment performance and cost under influent fluctuations. Specifically, we first developed five WRRFs, including activated sludge, activated sludge with chemical precipitation (ASCP), enhanced biological phosphorus removal (EBPR), EBPR with acetate addition (EBPR-A), and EBPR with struvite recovery (EBPR-S), in a high-fidelity simulation program (GPS-X). The five WRRFs were based on an existing plant that treats combined domestic and industrial wastewater. The ANNs have satisfactory performance in capturing nonlinear biological behaviors for all five WRRFs, even though the prediction performance (R-square) slightly decreases as the model complexity increases. We advanced ANNs application in WRRF models by simulating long-term (10-yr) performance with monthly influent fluctuations using ANNs trained by simulation data from steady-state models and evaluated their performance on Phosphorus (P) and Nitrogen (N) removal. EBPR-S shows the most resilience, while EBPR is more sensitive to influent characteristics impacted by stormwater inflow. When comparing life cycle costs of N and P removal for each layout over the 10-yr simulation period, EPBR-S is the most cost-effective alternative, highlighting both the operational and cost benefits of side-stream P recovery. By capturing both nonlinear behaviors of biological treatment and operating costs with computationally lean ANNs, this study provides a paradigm for integrating complex WRRF models within integrated watershed modeling frameworks.

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Section: Resultsmentioning

confidence: 99%

Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling

Emaminejad

Aguiar

et al. 2021

ACS EST Eng.

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“…ANN's flexibility and non-linear learning capabilities make this method coherent with research on forecasting (Gupta et al, 2017). The ANN, as a prevalent modelling method, has been used for the identification of the complicated non-linear relationship of inputs and output (Nourani et al, 2021).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A comparative study of forecasting methods using real-life econometric series data

Matta

Bianchesi

Oliveira

et al. 2021

Prod.

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Paper aims: This paper presents a comparative evaluation of different forecasting methods using two artificial neural networks (Multilayer Perceptron network and Radial Basis Functions Neural Network) and the Gaussian process regression.Originality: Due to the current world scenario, solving economic problems has become extremely important. Artificial neural networks are one of the most promising tools to forecast economic trends and are being widely studied in economic analyses. Therefore, due to the concerns about the performance of different forecasting methods to solve economic problems, this study contributes with an example of the forecasting performance of artificial neural network models compared with Gaussian process regression using Nelson-Plosser and U.S. macroeconomic real-life data sets.Research method: Two real-life data sets were used to evaluate the forecasting methods proposed in this paper. These data sets were normalised to values between zero and one. After that, the data training was performed and, once it was built, a model was used to generate forecasts. Thus, observations were made to verify how accurately the fitted model forecast the values. Main findings:The results obtained from the study show that, for all forecasting horizons, multi-layer perceptron networks and Gaussian process regression models had the most satisfactory results. On the other hand, the radial basis functions neural network model was unsuitable for econometric data. Implications for theory and practice:This study contributes to a discussion about artificial neural networks and Gaussian process regression models for econometric forecasting. Although artificial neural networks are mainly used in economic analyses, the results showed that not all models, such as radial basis functions neural networks, present good results. In addition, the regression of the Gaussian process showed promising results to forecast econometric data.

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“…In addition, we introduce here for the first time interval estimation using the neural network model for the conversion from VCD to global surface concentration of HCHO, increasing the credibility of the model by providing uncertainty information. This new idea can make up for the deficiency of inexplicability of the neural network model [62], thus being useful for the application of neural network models in the field of estimating atmospheric pollutants or health risk in the future.…”

Section: Limitations and Potential Improvementsmentioning

confidence: 99%

Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique

et al. 2021

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Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants in outdoor air. However, the lack of monitoring of the global surface concentration of HCHO is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or, for research on a global scale, too data-demanding. To alleviate this issue, we adopted neural networks to estimate the 2019 global surface HCHO concentration with confidence intervals, utilizing HCHO vertical column density data from TROPOMI, and in-situ data from HAPs (harmful air pollutants) monitoring networks and the ATom mission. Our results show that the global surface HCHO average concentration is 2.30 μg/m3. Furthermore, in terms of regions, the concentrations in the Amazon Basin, Northern China, South-east Asia, the Bay of Bengal, and Central and Western Africa are among the highest. The results from our study provide the first dataset on global surface HCHO concentration. In addition, the derived confidence intervals of surface HCHO concentration add an extra layer of confidence to our results. As a pioneering work in adopting confidence interval estimation to AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper paves the way for rigorous study of global ambient HCHO health risk and economic loss, thus providing a basis for pollution control policies worldwide.

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

Cited by 18 publications

References 70 publications

Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling

Evaluating Long-Term Treatment Performance and Cost of Nutrient Removal at Water Resource Recovery Facilities under Stochastic Influent Characteristics Using Artificial Neural Networks as Surrogates for Plantwide Modeling

A comparative study of forecasting methods using real-life econometric series data

Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique

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