Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan

Chang, Fi-John; Sun, Wei‐Zen; Chung, Chi-Jui

doi:10.1080/02626667.2013.775447

Cited by 17 publications

(5 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accurate simulation of evaporation contributes to many aspects including hydrology and water resources management, agricultural activities, irrigation scheduling, and water conservation, especially in arid regions [3,4]. However, evaporation is extremely difficult to present effectively due to its complex interactions between land and atmosphere system [5]. Nowadays, the methods for evaporation measurement are generally divided into estimation by models and direct measurement 3 of 17 E p prediction in Poyang Lake Basin of southern China.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm

Dong

Zeng

et al. 2021

Water

View full text Add to dashboard Cite

Accurate estimation of pan evaporation (Ep) is vital for the development of water resources and agricultural water management, especially in arid and semi-arid regions where it is restricted to set up the facilities and measure pan evaporation accurately and consistently. Besides, using pan evaporation estimating models and pan coefficient (kp) models is a classic method to assess the reference evapotranspiration (ET0) which is indispensable to crop growth, irrigation scheduling, and economic assessment. This study estimated the potential of a novel hybrid machine learning model Coupling Bat algorithm (Bat) and Gradient boosting with categorical features support (CatBoost) for estimating daily pan evaporation in arid and semi-arid regions of northwest China. Two other commonly used algorithms including random forest (RF) and original CatBoost (CB) were also applied for comparison. The daily meteorological data for 12 years (2006–2017) from 45 weather stations in arid and semi-arid areas of China, including minimum and maximum air temperature (Tmin, Tmax), relative humidity (RH), wind speed (U), and global solar radiation (Rs), were utilized to feed the three models for exploring the ability in predicting pan evaporation. The results revealed that the new developed Bat-CB model (RMSE = 0.859–2.227 mm·d−1; MAE = 0.540–1.328 mm·d−1; NSE = 0.625–0.894; MAPE = 0.162–0.328) was superior to RF and CB. In addition, CB (RMSE = 0.897–2.754 mm·d−1; MAE = 0.531–1.77 mm·d−1; NSE = 0.147–0.869; MAPE = 0.161–0.421) slightly outperformed RF (RMSE = 1.005–3.604 mm·d−1; MAE = 0.644–2.479 mm·d−1; NSE = −1.242–0.894; MAPE = 0.176–0.686) which had poor ability to operate the erratic changes of pan evaporation. Furthermore, the improvement of Bat-CB was presented more comprehensively and obviously in the seasonal and spatial performance compared to CB and RF. Overall, Bat-CB has high accuracy, robust stability, and huge potential for Ep estimation in arid and semi-arid regions of northwest China and the applications of findings in this study have equal significance for adjacent countries.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm

Dong

Zeng

et al. 2021

Water

View full text Add to dashboard Cite

show abstract

“…Besides, a DF-based model was also utilized for the analysis and prediction of survey-based consumer confidence [35]. Hybrid models that combine ANN and DF have also been developed for various applications, such as evaporation prediction [36] and performance comparison [37]. However, the challenge of selecting appropriate input factors to improve prediction accuracy persists.…”

Section: Related Workmentioning

confidence: 99%

Empowering Greenhouse Cultivation: Dynamic Factors and Machine Learning Unite for Advanced Microclimate Prediction

Sun,

Chang

2023

Water

View full text Add to dashboard Cite

Climate change has led to more frequent extreme weather events such as heatwaves, droughts, and storms, which significantly impact agriculture, causing crop damage. Greenhouse cultivation not only provides a manageable environment that protects crops from external weather conditions and pests but also requires precise microclimate control. However, greenhouse microclimates are complex since various heat transfer mechanisms would be difficult to model properly. This study proposes an innovative hybrid model (DF-RF-ANN), which seamlessly fuses three components: the dynamic factor (DF) model to extract unobserved factors, the random forest (RF) to identify key input factors, and a backpropagation neural network (BPNN) to predict greenhouse microclimate, including internal temperature, relative humidity, photosynthetically active radiation, and carbon dioxide. The proposed model utilized gridded meteorological big data and was applied to a greenhouse in Taichung, Taiwan. Two comparative models were configured using the BPNN and the Long short-term memory neural network (LSTM). The results demonstrate that DF-RF-ANN effectively captures the trends of the observations and generates predictions much closer to the observations compared to LSTM and BPNN. The proposed DF-RF-ANN model hits a milestone in multi-horizon and multi-factor microclimate predictions and offers a cost-effective and easily accessible approach. This approach could be particularly beneficial for small-scale farmers to make the best use of resources under extreme climatic events for contributing to sustainable development goals (SDGs) and the transition towards a green economy.

show abstract

“…Because of these several variables, estimating evaporation rate becomes more complex as it is totally non-linear. A number of scholars have tried to predict evaporation from different meteorological variables (Chang, Sun, & Chung, 2013;Kisi, 2006;Lin, Lin, & Wu, 2013). In recent years, machine learning approaches including Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Fuzzy Genetic (FG), Support Vector Regression (SVR) and the integrated models of these methods with wavelet or other data preprocessing approaches have been effectively applied in water related fields such as water resource engineering, prediction of suspended sediment load in rivers, forecasting monthly stream flow, estimating friction factor in irrigation pipes, PE modeling, etc.…”

Section: Introductionmentioning

confidence: 99%

Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates

Qasem

Samadianfard

Kheshtgar

et al. 2019

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

Evaporation rate is one of the key parameters in determining the ecological conditions and it has an irrefutable role in the proper management of water resources. In this paper, the efficiency of some data-driven techniques including support vector regression (SVR) and artificial neural networks (ANN) and combination of them with wavelet transforms (WSVR and WANN) were investigated for predicting evaporation rates at Tabriz (Iran) and Antalya (Turkey) stations. For evaluating the performances of studied techniques, four different statistical indicators were utilized namely the root mean square error (RMSE), the mean absolute error (MAE), the correlation coefficient (R), and Nash-Sutcliffe efficiency (NSE). Additionally, Taylor diagrams were implemented to test the similarity among the observed and predicted data. Outcomes showed that at Tabriz station, the ANN3 (third input combination that are air temperatures and solar radiation used by ANN) with RMSE of 0.701, MAE of 0.525, R of 0.990 and NSE of 0.977 had better performances in comparison with WANN, SVR and WSVR. So, the wavelet transforms did not have positive effects in increasing the precision of ANN and SVR predictions at Tabriz station. Also, approximately the same trend was seen at Antalya station. In other words, ANN5 (fifth input combination that are air temperatures, relative humidity and solar radiation used by ANN) with RMSE of 0.923, MAE of 0.697, R of 0.962 and NSE of 0.898 had a more accurate predictions among others. Conversely, wavelet transform reduced the prediction errors of SVR at Antalya station. So, the WSVR5 with RMSE of 1.027, MAE of 0.728, R of 0.950 and NSE of 0.870 predicted evaporation rates of Antalya station more precisely than other SVR models. As a conclusion, results from the current study proved that ANN provided reasonable trends for evaporation modeling at both Tabriz and Antalya stations.

show abstract

Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan

Cited by 17 publications

References 58 publications

Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm

Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm

Empowering Greenhouse Cultivation: Dynamic Factors and Machine Learning Unite for Advanced Microclimate Prediction

Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates

Contact Info

Product

Resources

About