scholarly journals Heuristic methods applied in reference evapotranspiration modeling

2018 ◽  
Vol 42 (3) ◽  
pp. 314-324 ◽  
Author(s):  
Daniel Althoff ◽  
Helizani Couto Bazame ◽  
Roberto Filgueiras ◽  
Santos Henrique Brant Dias
Keyword(s):  
Reference Evapotranspiration ◽  
Performance Criteria ◽  
Superior Performance ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Computational Techniques ◽  
Heuristic Methods ◽  
Scarce Data

ABSTRACT The importance of the precise estimation of evapotranspiration is directly related to sustainable water usage. Since agriculture represents 70% of Brazil’s water consumption, adequate and efficient application of water may reduce the conflicts over the use of water among the multiple users. Considering the importance of accurate estimation of evapotranspiration, the objective of the present study was to model and compare the reference evapotranspiration from different heuristic methodologies. The standard Penman-Monteith method was used as reference for evapotranspiration, however, to evaluate the heuristic methodologies with scarce data, two widely known methods had their performances assessed in relation to Penman-Monteith. The methods used to estimate evapotranspiration from scarce data were Priestley-Taylor and Thornthwaite. The computational techniques Stepwise Regression (SWR), Random Forest (RF), Cubist (CB), Bayesian Regularized Neural Network (BRNN) and Support Vector Machines (SVM) were used to estimate evapotranspiration with scarce and full meteorological data. The results show the robustness of the heuristic methods in the prediction of the evapotranspiration. The performance criteria of machine learning methods for full weather data varied from 0.14 to 0.22 mm d-1 for mean absolute error (MAE), from 0.21 to 0.29 mm d-1 for root mean squared error (RMSE) and from 0.95 to 0.99 coefficient of determination (r²). The computational techniques proved superior performance to established methods in literature, even in scenarios of scarce variables. The BRNN presented the best performance overall.

Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression

2020 ◽  
Vol 38 (8) ◽  
pp. 840-850 ◽  
Author(s):  
Zeynep Ceylan
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Machine Learning Algorithms ◽  
Economic Indicators ◽  
Bayesian Optimization ◽  
Superior Performance ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector

Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization was used to forecast the MSW generation of Turkey. The Bayesian optimization method, which can efficiently optimize the hyperparameters of kernel functions in the machine learning algorithms, was applied to reduce the computation redundancy and enhance the estimation performance of the models. Four socio-economic indicators such as population, gross domestic product per capita, inflation rate, and the unemployment rate were used as input variables. The performance of the Bayesian GPR (BGPR) model was compared with the multiple linear regression (MLR) and Bayesian support vector regression (BSVR) models. Different performance measures such as mean absolute deviation (MAD), root mean square error (RMSE), and coefficient of determination (R2) values were used to evaluate the performance of the models. The exponential-GPR model tuned by Bayesian optimization showed superior performance with minimum MAD (0.0182), RMSE (0.0203), and high R2 (0.9914) values in the training phase and minimum MAD (0.0342), RMSE (0.0463), and high R2 (0.9841) values in the testing phase. The results of this study can help decision-makers to be aware of social-economic factors associated with waste management and ensure optimal usage of their resources in future planning.

scholarly journals Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)

2018 ◽  
Vol 19 (2) ◽  
pp. 392-403 ◽  
Author(s):  
Omolbani Mohammadrezapour ◽  
Jamshid Piri ◽  
Ozgur Kisi
Keyword(s):  
Fuzzy Inference ◽  
Climatic Factors ◽  
Potential Evapotranspiration ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Heuristic Methods ◽  
Inference System

Abstract Evapotranspiration is an important component in planning and management of water resources. It depends on climatic factors and the influence of these factors on each other makes evapotranspiration estimation difficult. This study attempts to explore the possibility of predicting this important component using three different heuristic methods: support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). In this regard, according to the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith equation, the monthly potential evapotranspiration in four synoptic stations (Zahedan, Zabol, Iranshahr, and Chabahar) was calculated using monthly weather data. The weather data were then used as inputs to the SVM, ANFIS and GEP models to estimate potential evapotranspiration. Five different input combinations were tried in the applications. The results of SVM, ANFIS and GEP models were compared based on the coefficient of determination (R2), mean absolute error and root mean square error. Findings showed that the SVM model, whose inputs are average air temperature, relative humidity, wind speed, and sunny hours of the current and one previous month, performed better than the other models for the Zahedan, Zabol, Iranshahr, and Chabahar stations. Comparison of the three heuristic methods indicated that in all stations, the SVM, GEP and ANFIS models took first, second, and third place in estimation of the monthly potential evapotranspiration, respectively.

scholarly journals Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5763 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Indrajit Chowdhuri ◽  
Zhaleh Siabi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Resource Management ◽  
Nitrate Concentration ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Agricultural Areas

Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

scholarly journals Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation

2021 ◽  
Vol 25 (2) ◽  
pp. 603-618
Author(s):  
Mohammad Taghi Sattari ◽  
Halit Apaydin ◽  
Shahab S. Band ◽  
Amir Mosavi ◽  
Ramendra Prasad
Keyword(s):  
Minimum Temperature ◽  
Short Term Memory ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Accurate Estimation ◽  
Support Vector ◽  
Learning Approaches ◽  
Ann Model ◽  
Average Maximum ◽  
Semi Arid

Abstract. Timely and accurate estimation of reference evapotranspiration (ET0) is indispensable for agricultural water management for efficient water use. This study aims to estimate the amount of ET0 with machine learning approaches by using minimum meteorological parameters in the Corum region, which has an arid and semi-arid climate and is regarded as an important agricultural centre of Turkey. In this context, monthly averages of meteorological variables, i.e. maximum and minimum temperature; sunshine duration; wind speed; and average, maximum, and minimum relative humidity, are used as inputs. Two different kernel-based methods, i.e. Gaussian process regression (GPR) and support vector regression (SVR), together with a Broyden–Fletcher–Goldfarb–Shanno artificial neural network (BFGS-ANN) and long short-term memory (LSTM) models were used to estimate ET0 amounts in 10 different combinations. The results showed that all four methods predicted ET0 amounts with acceptable accuracy and error levels. The BFGS-ANN model showed higher success (R2=0.9781) than the others. In kernel-based GPR and SVR methods, the Pearson VII function-based universal kernel was the most successful (R2=0.9771). Scenario 5, with temperatures including average temperature, maximum and minimum temperature, and sunshine duration as inputs, gave the best results. The second best scenario had only the sunshine duration as the input to the BFGS-ANN, which estimated ET0 having a correlation coefficient of 0.971 (Scenario 8). Conclusively, this study shows the better efficacy of the BFGS in ANNs for enhanced performance of the ANN model in ET0 estimation for drought-prone arid and semi-arid regions.

Introducing a Hybrid Model SAE-BP for Regression Analysis of Soil Temperature With Hyperspectral Data

2020 ◽  
Vol 11 (3) ◽  
pp. 66-79 ◽  
Author(s):  
Miaomiao Ji ◽  
Keke Zhang ◽  
Qiufeng Wu
Keyword(s):  
Remote Sensing ◽  
Soil Temperature ◽  
Hybrid Model ◽  
Terrestrial Ecosystems ◽  
Hyperspectral Data ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Bp Model

Soil temperature, as one of the critical meteorological parameters, plays a key role in physical, chemical and biological processes in terrestrial ecosystems. Accurate estimation of dynamic soil temperature is crucial for underground soil ecological research. In this work, a hybrid model SAE-BP is proposed by combining stacked auto-encoders (SAE) and back propagation (BP) algorithm to estimate soil temperature using hyperspectral remote sensing data. Experimental results show that the proposed SAE-BP model achieves a more stable and effective performance than the existing logistic regression (LR), support vector regression (SVR) and BP neural network with an average value of mean square error (MSE) = 1.926, mean absolute error (MAE) = 0.962 and coefficient of determination (R2) = 0.910. In addition, the effect of hidden structures and labeled training data ratios in SAE-BP is further explored. The SAE-BP model demonstrates the potential in high-dimensional and small hyperspectral datasets, representing a significant contribution to soil remote sensing.

scholarly journals Estimating models for reference evapotranspiration with core meteorological parameters via path analysis

2016 ◽  
Vol 48 (2) ◽  
pp. 340-354 ◽  
Author(s):  
Ye Liu ◽  
Miao Yu ◽  
Xiaoyi Ma ◽  
Xuguang Xing
Keyword(s):  
Neural Network ◽  
Path Analysis ◽  
Meteorological Parameters ◽  
Reference Evapotranspiration ◽  
Southern China ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Decision Variable ◽  
Cent Error

Accurate estimation and reliable universal performance of reference evapotranspiration (ET0) obtained from a few meteorological parameters are important for the rational planning of agricultural water resources and the effective management of water in irrigated regions. Meteorological data in southern China were used to calculate ET0 using the standard Penman–Monteith formula and determined the core decision variable (hours of sunshine, N) and the limited decision variable (relative humidity, RH) using path analysis. Estimation models using an artificial neural network and wavelet neural network were established for the Wuhan and Guangzhou meteorological stations. The statistical indices were positively correlated with the decision contribution rates to ET0. The ET0 values for other stations in southern China were all estimated by these models, which were trained for the Guangzhou station, and then made a total comparison with Hargreaves–Samani (HS) and Priestley–Taylor (PT) empirical ET0 models. Error analysis indicated that the root mean square error and the mean absolute per cent error were around 0.32 mm and 5.5%, respectively, with a high coefficient of determination and Nash–Sutcliffe efficiency over 0.9, indicating that these estimating models could be applied in more regions for universal analysis with high accuracy.

Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area

2016 ◽  
Vol 48 (5) ◽  
pp. 1177-1191 ◽  
Author(s):  
Zhenliang Yin ◽  
Xiaohu Wen ◽  
Qi Feng ◽  
Zhibin He ◽  
Songbing Zou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Air Temperature ◽  
Reference Evapotranspiration ◽  
Accurate Estimation ◽  
Support Vector ◽  
Climatic Data ◽  
Neural Network Models ◽  
Mountain Area ◽  
Semi Arid

Accurate estimation of evapotranspiration is vitally important for management of water resources and environmental protection. This study investigated the accuracy of integrating genetic algorithm and support vector machine (GA-SVM) models using climatic variables for simulating daily reference evapotranspiration (ET0). The developed GA-SVM models were tested using the ET0 calculated by Penman–Monteith FAO-56 (PMF-56) equation in a semi-arid environment of Qilian Mountain, northwest China. Eight models were developed using different combinations of daily climatic data including maximum air temperature (Tmax), minimum air temperature (Tmin), wind speed (U2), relative humidity (RH), and solar radiation (Rs). The accuracy of the models was evaluated using root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (r). The results indicated that the GA-SVM models successfully estimated ET0 with those obtained by the PMF-56 equation in the semi-arid mountain environment. The model with input combinations of Tmin, Tmax, U2, RH, and Rs had the smallest value of the RMSE and MAE as well as higher value of r (0.995) compared to other models. Relative to the performance of support vector machine (SVM) models and feed-forward artificial neural network models, it was found that the GA-SVM models proved superior for simulating ET0.

Estimation of reference evapotranspiration using Aquacrop model for agro-climatic conditions of Madhya Pradesh

2017 ◽  
Vol 51 (06) ◽  
Author(s):  
Deepika Yadav ◽  
M. K. Awasthi ◽  
R. K. Nema
Keyword(s):  
Temporal Variability ◽  
Reference Evapotranspiration ◽  
Madhya Pradesh ◽  
Weather Data ◽  
Accurate Estimation ◽  
Spatial And Temporal Variability ◽  
Statistical Parameters ◽  
Climatic Zones ◽  
Aquacrop Model ◽  
Daily Weather Data

Accurate estimation of evapotranspiration is necessary step for better management and allocation of water resources. The United Nations Food and Agriculture Organization (FAO) adopted the Penman Moneith method as a global standard to estimate reference crop evapotranspiration (ETo). The study aimed to estimate FAO P-M reference evapotranspiration for different district of five agro climatic zones of Madhya Pradesh state by using Aquacrop model. Daily weather data including maximum and minimum temperature, precipitation, relative humidity, wind speed and solar radiation were collected for the period of 1979 to 2013 which were used as input data in Aquacrop. Several statistical parameters were used for characterizing the spatial and temporal variability of ETo. The average monthly ETo was found maximum in month of May (10.67 mm day-1) in all district of different agro climatic zones for the average period considered for the study and also for each years, whereas average minimum ETo was estimated in month of December (3.23 mm day-1) in Kymore Plateau and August (2.44 mm day-1) in Satpura Plateau. The mean daily reference evapotranspiration ranges from 4 mm day-1 to 10 mm day-1 for all districts. From the statistical analysis it was found that spatial variability of ETo lower than the temporal variability. It means the bigger differentiation of ETo in the years than in the space.

Modeling Reference Evapotranspiration in a Semiarid Area Using Data-driven Techniques: A Case Study in Lower Cheliff plain, Algeria

2021 ◽  
Author(s):  
Mohammed ACHITE ◽  
Muhammad Taghi Sattari ◽  
Abderrezak Kamel Toubal ◽  
Andrzej Wałęga ◽  
Nir Krakauer ◽  
...  
Keyword(s):  
Neural Network ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Gene Expression Programming ◽  
Global Radiation ◽  
Performance Criteria ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Irrigated Agriculture ◽  
Efficiency Coefficient

Abstract Evapotranspiration (ET) is an important part of the hydrologic cycle, especially when it comes to irrigated agriculture. For the estimation of reference evapotranspiration (ET0), direct methods either pose difficulties or call for many inputs that may not always be available from weather stations. This study compares Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN). and Gene Expression Programming (GEP) approachs for the estimation of daily ET0 in a weather station in Lower Cheliff plain (northwest Algeria), over a 6-year period (2006–2011). Firstly, measured air temperature, relative humidity, wind speed, solar radiation and global radiation was used to calculate ET0 using FAO-56 Penman-Monteith equation as the reference. Then, the calculated ET0 using FAO-56 Penman-Monteith was considered as output for data driven models, while the measured meteorological data were considered as input of the models. The coefficient of determination (R2), root mean square error (RMSE) and Nash Sutcliffe efficiency coefficient (EF) were used to evaluate the developed models. The results of the developed models were compared with the Penman-Monteith evapotranspiration using these performance criteria. The FFNN model proved to yield the best performance compared to all the developed data-driven models, while the RBF-NN and GEP models also demonstrated potential for good performance.

scholarly journals DESEMPENHO DE MÉTODOS NA ESTIMATIVA DE EVAPOTRANSPIRAÇÃO DE REFERÊNCIA PARA O ESTADO DA PARAÍBA, BRASIL

Irriga ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 481 ◽  
Author(s):  
Geffson De Figueredo Dantas ◽  
Vinicius Mendes Rodrigues de Oliveira ◽  
Alexandre Barcellos Dalri ◽  
Luiz Fabiano Palaretti ◽  
Miqueias Gomes dos Santos ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Standard Method ◽  
Reference Evapotranspiration ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Empirical Methods ◽  
Satisfactory Performance ◽  
Time Period ◽  
And Performance ◽  
Level Of Agreement

DESEMPENHO DE MÉTODOS NA ESTIMATIVA DE EVAPOTRANSPIRAÇÃO DE REFERÊNCIA PARA O ESTADO DA PARAÍBA, BRASIL  GEFFSON DE FIGUEREDO DANTAS1; VINICIUS MENDES RODRIGUES DE OLIVEIRA2; ALEXANDRE BARCELLOS DALRI3; LUIZ FABIANO PALARETTI3; MIQUEIAS GOMES DOS SANTOS4 E ROGÉRIO TEXEIRA DE FARIA3 1 Licenciado em Ciências Agrárias, Doutorando em Agronomia (Ciência do Solo), Departamento de Engenharia Rural, FCAV-Unesp/ Jaboticabal, SP, [email protected] Engº Agrônomo, Doutorando em Engenharia Agrícola, Departamento de Engenharia Agrícola, UFV/Viçosa, MG, [email protected] Engº Agrícola, Prof. Doutor, Departamento de Engenharia Rural, FCAV-Unesp/Jaboticabal, SP, [email protected], [email protected], [email protected] Engº Agrônomo, Doutorando em Agronomia (Ciência do Solo), Departamento de Engenharia Rural, FCAV-Unesp/Jaboticabal, SP, [email protected]  1 RESUMO O métodos padrão de estimativa da evapotranspiração de referência (ET0) FAO Penman-Monteith (PM) emprega variáveis meteorológicas as quais nem sempre estão disponíveis à maioria dos produtores rurais. Assim, o presente trabalho teve por objetivo avaliar seu desempenho e aferir se necessário, para as condições da Paraíba, diferentes métodos para a estimativa diária de ET0, comparando-os com o método padrão FAO Penman-Monteith. Utilizou-se uma série histórica de 17 anos de dados meteorológicos do INMET de quatro cidades do Estado da Paraíba. Os métodos avaliados foram os de Hargreaves-Samani (HS), Blaney-Criddle (BC), Camargo (C) e Jensen-Haise (JH). Para análise comparativa entre o método PM e os outros métodos, foi realizada a análise de correlação e regressão linear e do coeficiente de determinação (R²). Para a exatidão dos métodos empíricos, foi realizada a análise para a determinação do índice de concordância (d) e do índice de desempenho (c). A ET0, obtida pelos quatro métodos empíricos obtiveram desempenho satisfatório, o método HS para a mesorregião do agreste não necessitou de ajustes, já os métodos JH, C e HS para as mesorregiões litoral, borborema e sertão necessitaram de ajuste para melhor acurácia em relação ao método padrão. Palavras-chave: calibração,  irrigação, Penman-Monteith  DANTAS, G. de F.; OLIVEIRA, V. M. R. de; DALRI, A. B.; PALARETTI, L. F.; SANTOS, M. G. dos; FARIA, R. T. dePERFORMANCE OF METHODS FOR ESTIMATING ET0 IN PARAÍBA STATE, BRAZIL  2 ABSTRACT The standard method for estimating reference evapotranspiration (ET0), FAO Penman-Monteith (PM) employs meteorological variables which are not always available to most farmers. Thus, this study aimed to evaluate and benchmark their performance if necessary, to the conditions of Paraíba, different methods for daily ET0 were estimated by comparing them with the standard FAO Penman-Monteith method. We used a time period of 17 years of weather data INMET in four cities in the state of Paraíba. The methods evaluated were the Hargreaves-Samani (HS), Blaney-Criddle (BC), Camargo (C) and Jensen-Haise (JH). For comparative analysis between the PM method and other methods, correlation analysis and linear regression to determine the coefficients of the equation (Y = a + bx) and the coefficient of determination (R²) was performed. For accuracy of empirical methods, the analysis was performed to determine the level of agreement (d) and performance index (c). The estimation of reference evapotranspiration, obtained by four empirical for meso-Paraíba PB methods, achieved satisfactory performance, the HS method for the rough mesoregion needed no adjustments since the JH, C and HS methods for meso coast, borborema and hinterland need adjustment for better accuracy compared to the standard method. Keywords: calibration, irrigation, evapotranspiration

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