scholarly journals Estimating evapotranspiration using the complementary relationship and the Budyko framework

2017 ◽  
Vol 8 (4) ◽  
pp. 771-790 ◽  
Author(s):  
Homin Kim ◽  
Jagath J. Kaluarachchi
Keyword(s):  
Root Mean Square Error ◽  
Vegetation Cover ◽  
Meteorological Data ◽  
Mean Square ◽  
Limited Data ◽  
Complementary Relationship ◽  
Proposed Model ◽  
The Usa ◽  
Dry Conditions ◽  
The Subject

Abstract Several models have been developed to estimate evapotranspiration. Among those, the complementary relationship has been the subject of many recent studies because it relies on meteorological data only. Recently, the modified Granger and Gray (GG) model showed its applicability across 34 diverse global sites. While the modified GG model showed better performances compared to the recently published studies, it can be improved for dry conditions and the relative evaporation parameter in the original GG model needs to be further investigated. This parameter was empirically derived from limited data from wet environments in Canada – a possible reason for decreasing performance with dry conditions. This study proposed a refined GG model to overcome the limitation using the Budyko framework and vegetation cover to describe relative evaporation. This study used 75 eddy covariance sites in the USA from AmeriFlux, representing 36 dry and 39 wet sites. The proposed model produced better results with decreasing monthly mean root mean square error of about 30% for dry sites and 15% for wet sites compared to the modified GG model. The proposed model in this study maintains the characteristics of the Budyko framework and the complementary relationship and produced improved evapotranspiration estimates under dry conditions.

scholarly journals Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245270
Author(s):  
Lucas Borges Ferreira ◽  
Fernando França da Cunha ◽  
Sidney Sara Zanetti
Keyword(s):  
Root Mean Square Error ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Irrigation Scheduling ◽  
Soil Water Balance ◽  
Suitable Model ◽  
Mean Square ◽  
Error Metrics ◽  
Performance Gains ◽  
Better Than

Alternative models for the estimation of reference evapotranspiration (ETo) are typically assessed using traditional error metrics, such as root mean square error (RMSE), which may not be sufficient to select the best model for irrigation scheduling purposes. Thus, this study analyzes the performance of the original and calibrated Hargreaves-Samani (HS), Romanenko (ROM) and Jensen-Haise (JH) equations, initially assessed using traditional error metrics, for use in irrigation scheduling, considering the simulation of different irrigation intervals/time scales. Irrigation scheduling was simulated using meteorological data collected in Viçosa-MG and Mocambinho-MG, Brazil. The Penman-Monteith FAO-56 equation was used as benchmark. In general, the original equations did not perform well to estimate ETo, except the ROM and HS equations used at Viçosa and Mocambinho, respectively. Calibration and the increase in the time scale provided performance gains. When applied in irrigation scheduling, the calibrated HS and JH equations showed the best performances. Even with greater errors in estimating ETo, the calibrated HS equation performed similarly or better than the calibrated JH equation, as it had errors with greater potential to be canceled during the soil water balance. Finally, in addition to using error metrics, the performance of the models throughout the year should be considered in their assessment. Furthermore, simulating the application of ETo models in irrigation scheduling can provide valuable information for choosing the most suitable model.

scholarly journals Machine Learning Based Prediction of Suicide Probability

2020 ◽  
Vol 10 (1) ◽  
pp. 94-97
Keyword(s):  
Machine Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Regression Models ◽  
Mean Square ◽  
Spline Regression ◽  
Proposed Model ◽  
Bayesian Machine Learning

Many factors have led to the increase of suicide-proneness in the present era. As a consequence, many novel methods have been proposed in recent times for prediction of the probability of suicides, using different metrics. The current work reviews a number of models and techniques proposed recently, and offers a novel Bayesian machine learning (ML) model for prediction of suicides, involving classification of the data into separate categories. The proposed model is contrasted against similar computationally-inexpensive techniques such as spline regression. The model is found to generate appreciably accurate results for the dataset considered in this work. The application of Bayesian estimation allows the prediction of causation to a greater degree than the standard spline regression models, which is reflected by the comparatively low root mean square error (RMSE) for all estimates obtained by the proposed model.

scholarly journals On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

2008 ◽  
Vol 12 (6) ◽  
pp. 1339-1351 ◽  
Author(s):  
G. Laguardia ◽  
S. Niemeyer
Keyword(s):  
Soil Moisture ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Land Surface ◽  
Soil Depth ◽  
Meteorological Data ◽  
Data Retrieval ◽  
Mean Square ◽  
Mountainous Regions

Abstract. In order to evaluate the reliability of the soil moisture product obtained by means of the LISFLOOD hydrological model (De Roo et al., 2000), we compare it to soil moisture estimates derived from ERS scatterometer data (Wagner et al., 1999b). Once evaluated the effect of scale mismatch, we calculate the root mean square error and the correlation between the two soil moisture time series on a pixel basis and we assess the fraction of variance that can be explained by a set of input parameter fields that vary from elevation and soil depth to rainfall statistics and missing or snow covered ERS images. The two datasets show good agreement over large regions, with 90% of the area having a positive correlation coefficient and 66% having a root mean square error minor than 0.5 pF units. Major inconsistencies are located in mountainous regions such as the Alps or Scandinavia where both the methodologies suffer from insufficiently resolved land surface processes at the given spatial resolution, as well as from limited availability of satellite data on the one hand and the uncertainties in meteorological data retrieval on the other hand.

scholarly journals Modelling runoff in an arid watershed through integrated support vector machine

2020 ◽  
Author(s):  
Sandeep Samantaray ◽  
Dillip K. Ghose
Keyword(s):  
Support Vector Machine ◽  
Phase Space ◽  
Root Mean Square Error ◽  
Firefly Algorithm ◽  
Support Vector ◽  
Mean Square ◽  
Monsoon Precipitation ◽  
Suggested Model ◽  
Hydrologic Processes ◽  
Proposed Model

Abstract Modelling of runoff is a significant practice in water resources engineering. Therefore, discovering consistent and advanced methods for prediction of runoff is crucial for hydrologic processes. Here, a narrative integrated intelligence model attached with PSR (phase space reconstruction) is anticipated to estimate runoff for five watersheds of Balangir, Odisha, India. Monthly monsoon precipitation, temperature, humidity data of five watersheds over 28 years (1990–2017) are employed and validated. Here, the proposed model is an integration of support vector machine (SVM) with firefly algorithm (FFA) and PSR. Various indices like NSE (Nash–Sutcliffe), RMSE (root mean square error) and WI (Willmott's index) are used to find the performance of the model. The developed PSR-SVM-FFA model demonstrates pre-eminent WI value ranging from 0.97 to 0.98 while the SVM and SVM-FFA models encompass 0.92 to 0.93 and 0.94 to 0.95, respectively. Also an assessment of data from the suggested model is schemed and validated. The proposed PSR-SVM-FFA model gives better accuracy results and error limiting up to 2–3%.

scholarly journals DEVELOPMENT OF RAINFALL FORECASTING MODEL USING MACHINE LEARNING WITH SINGULAR SPECTRUM ANALYSIS

2022 ◽  
Vol 23 (1) ◽  
pp. 172-186
Author(s):  
Pundru Chandra Shaker Reddy ◽  
Sucharitha Yadala ◽  
Surya Narayana Goddumarri
Keyword(s):  
Random Forest ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Support Vector ◽  
Mean Square ◽  
Singular Spectrum ◽  
Proposed Model

Agriculture is the key point for survival for developing nations like India. For farming, rainfall is generally significant. Rainfall updates are help for evaluate water assets, farming, ecosystems and hydrology. Nowadays rainfall anticipation has become a foremost issue. Forecast of rainfall offers attention to individuals and knows in advance about rainfall to avoid potential risk to shield their crop yields from severe rainfall. This study intends to investigate the dependability of integrating a data pre-processing technique called singular-spectrum-analysis (SSA) with supervised learning models called least-squares support vector regression (LS-SVR), and Random-Forest (RF), for rainfall prediction. Integrating SSA with LS-SVR and RF, the combined framework is designed and contrasted with the customary approaches (LS-SVR and RF). The presented frameworks were trained and tested utilizing a monthly climate dataset which is separated into 80:20 ratios for training and testing respectively. Performance of the model was assessed using Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) and the proposed model produces the values as 71.6 %, 90.2 % respectively. Experimental outcomes illustrate that the proposed model can productively predict the rainfall. ABSTRAK:Pertanian adalah titik utama kelangsungan hidup negara-negara membangun seperti India. Untuk pertanian, curah hujan pada amnya ketara. Kemas kini hujan adalah bantuan untuk menilai aset air, pertanian, ekosistem dan hidrologi. Kini, jangkaan hujan telah menjadi isu utama. Ramalan hujan memberikan perhatian kepada individu dan mengetahui terlebih dahulu mengenai hujan untuk menghindari potensi risiko untuk melindungi hasil tanaman mereka dari hujan lebat. Kajian ini bertujuan untuk menyelidiki kebolehpercayaan mengintegrasikan teknik pra-pemprosesan data yang disebut analisis-spektrum tunggal (SSA) dengan model pembelajaran yang diawasi yang disebut regresi vektor sokongan paling rendah (LS-SVR), dan Random-Forest (RF), ramalan hujan. Menggabungkan SSA dengan LS-SVR dan RF, kerangka gabungan dirancang dan dibeza-bezakan dengan pendekatan biasa (LS-SVR dan RF). Kerangka kerja yang disajikan dilatih dan diuji dengan menggunakan set data iklim bulanan yang masing-masing dipisahkan menjadi nisbah 80:20 untuk latihan dan ujian. Prestasi model dinilai menggunakan Root Mean Square Error (RMSE) dan Nash – Sutcliffe Efficiency (NSE) dan model yang dicadangkan menghasilkan nilai masing-masing sebanyak 71.6%, 90.2%. Hasil eksperimen menggambarkan bahawa model yang dicadangkan dapat meramalkan hujan secara produktif.

scholarly journals Complementary Relationship for Estimating Evapotranspiration Using the Granger-Gray Model: Improvements and Comparison with a Remote Sensing Method

2017 ◽  
Author(s):  
Homin Kim ◽  
Jagath J. Kaluarachchi
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Climatic Conditions ◽  
Correction Function ◽  
Complementary Relationship ◽  
Operational Conditions ◽  
The Usa

Abstract. The Granger and Gray (GG) model, which uses the complementary relationship for estimating evapotranspiration (ET), is a simple approach requiring only commonly available meteorological data; however, most complementary relationship models decrease in predictive power with increasing aridity. In this study, a previously developed modified GG model using the vegetation index is further improved to estimate ET under a variety of climatic conditions. This updated GG model, GG-NDVI, includes Normalized Difference Vegetation Index (NDVI), precipitation, and potential evapotranspiration using the Budyko framework. The Budyko framework is consistent with the complementary relationship and performs well under dry conditions. We validated the GG-NDVI model under operational conditions with the commonly used remote sensing-based Operational Simplified Surface Energy Balance (SSEBop) model at 60 Eddy Covariance AmeriFlux sites located in the USA. Results showed that the Root Mean Square Error (RMSE) for GG-NDVI ranged between 15 and 20 mm month−1, which is lower than for SSEBop every year. Although the magnitude of agreement seems to vary from site to site and from season to season, the occurrences of RMSE less than 20 mm month−1 with the proposed model are more frequent than with SSEBop in both dry and wet sites. This study also found an inherent limitation of the complementary relationship under moist conditions, indicating the relationship is not symmetrical as previously suggested. A nonlinear correction function was incorporated into GG-NDVI to overcome this limitation. The resulting Adjusted GG-NDVI produced much lower RMSE values, along with lower RMSE across more sites, as compared to measured ET and SSEBop.

scholarly journals Evaluation of three grass growth models to predict grass growth in Ireland

2012 ◽  
Vol 151 (1) ◽  
pp. 91-104 ◽  
Author(s):  
C. HURTADO-URIA ◽  
D. HENNESSY ◽  
L. SHALLOO ◽  
R. P. O. SCHULTE ◽  
L. DELABY ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Prediction Error ◽  
Growth Models ◽  
Meteorological Data ◽  
Measured Data ◽  
Mechanistic Models ◽  
Mean Square ◽  
Growth Data ◽  
Grass Growth ◽  
Accuracy Of Prediction

SUMMARYGrass growth in temperate regions is highly seasonal and difficult to predict. A model that can predict grass growth from week to week would offer a valuable management and budgeting tool for grassland farmers. Many grass growth models have been developed, varying from simple empirical to complex mechanistic models. Three published grass growth models developed for perennial ryegrass swards in temperate climates were selected for evaluation: Johnson & Thornley (1983) (J&T model), Jouven et al. (2006) (J model) and Brereton et al. (1996) (B model). The models were evaluated using meteorological data and grass growth data from Teagasc Moorepark as a framework for further refinement for Irish conditions. The accuracy of prediction by the models was assessed using root mean square error (RMSE) and mean square prediction error (MSPE). The J&T model over-predicted grass growth in all 5 years examined and predicted a high primary grass growth peak, while the J and B models predicted grass growth closer to that measured. Overall, the J model had the smallest RMSE in 3 of the 5 years and the B model in 2 of the 5 years. In spring (February–April), the B model had the lowest RMSE and MSPE. In mid-season (April–August), the B model had the closest prediction to measured data (lowest RMSE), while in autumn (August–October) the J model had the closest prediction. The models with the greatest potential for grass growth prediction in Ireland, albeit with some modifications, are the J model and the B model.

scholarly journals On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

2008 ◽  
Vol 5 (3) ◽  
pp. 1227-1265 ◽  
Author(s):  
G. Laguardia ◽  
S. Niemeyer
Keyword(s):  
Soil Moisture ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Land Surface ◽  
Soil Depth ◽  
Meteorological Data ◽  
Data Retrieval ◽  
Mean Square ◽  
Mountainous Regions

Abstract. In order to evaluate the reliability of the soil moisture product obtained by means of the LISFLOOD hydrological model (De Roo et al., 2000), we compare it to soil moisture estimates derived from ERS scatterometer data (Wagner et al., 1999). Once calculated the root mean square error and the correlation between the two soil moisture time series on a pixel basis, we assess the fraction of variance that can be explained by a set of input parameter fields that vary from elevation and soil depth to rainfall statistics and missing or snow covered ERS images. The two datasets show good agreement over large regions, with 90% of the area having a positive correlation coefficient and 66% having a root mean square error minor than 0.5. Major inconsistencies are located in mountainous regions such as the Alps or Scandinavia where both the methodologies suffer from insufficiently resolved land surface processes at the given spatial resolution, as well as from limited availability of satellite data on the one hand and the uncertainties in meteorological data retrieval on the other hand.

СУБ’ЄКТ-ОРІЄНТОВАНА МОДЕЛЬ ПСИХОКОРЕКЦІЇ ГІПЕРАКТИВНОСТІ ДОШКІЛЬНИКІВ

1970 ◽  
Vol 7 (1) ◽  
pp. 70-79
Author(s):  
Людмила Прокопів
Keyword(s):  
Psychosocial Development ◽  
Special Educational Needs ◽  
Hyperactivity Disorder ◽  
Complex Approach ◽  
Proposed Model ◽  
Special Programs ◽  
Developmental Impairment ◽  
The Subject ◽  
Comprehensive Programs ◽  
Theoretical Foundations

У статті визначено актуальність та необхідність розробки і впровадження сучасних комплексних програм психологічної корекції для дошкільників з гіперактивним розладом і дефіцитом уваги (ГРДУ). Підкреслено неможливість надання ефективної допомоги дітям з ГРДУ без урахування їх особливих потреб, пов’язаних з освітою. Обґрунтовано теоретичні засади суб’єкт-орієнтованої моделі психокорекції гіперактивності дошкільників шляхом синтезу патогенетичного і психолого-педагогічного підходів на базі принципу інтегральної суб’єктності З. С. Карпенко. Запропонована модель психокорекції гіперактивності пояснює гальмування і деформацію психосоціального розвитку дошкільника затримкою розвитку його суб’єктності в сенсомоторному, атенціональному, мнемічному, афективно-конативному, мовленнєвому, комунікативному аспектах. Аргументовано необхідність здійснення комплексного підходу до психологічної корекції, виховання і навчання гіперактивних дошкільників і впровадження спеціальних програм психоедукації для батьків. The paper highlights the topicality and need for elaboration and implementation of up-to-date comprehensive programs of psychological correction for preschool children with Attention Deficit/Hyperactivity Disorder (ADHD). Failure to provide effective aid for children with ADHD without regard to their special educational needs has been underlined. Theoretical foundations of the subject-oriented model of psychocorrection of preschool children’s hyperactivity by means of synthesis of pathogenic and psychological-pedagogical approaches on the basis of Z. S. Karpenko’s principle of integral subjectivity have been validated. The proposed model of psychocorrection of hyperactivity explains developmental impairment and deformation of psychosocial development of preschoolers by a delay of their subjectivity in terms of sensomotor, attentional, mnemic, affective-conative, speech and communicative aspects. The need for actualization of a complex approach towards psychological correction, upbringing and education of hyperactive preschoolers and implementation of special programs of psychological education for parents has been reasoned.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

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