Estimation of Monthly Mean Reference Evapotranspiration in Turkey

Monthly mean reference evapotranspiration (ET ₀ ) is estimated using adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN) models. Various combinations of long-term average monthly climatic data of wind speed, air temperature, relative humidity, and solar radiation, recorded at stations in Turkey, are used as inputs to the ANFIS and ANN models so as to calculate ET ₀ given by the FAO-56 PM (Penman-Monteith) equation. First, a comparison is made among the estimates provided by the ANFIS and ANN models and those by the empirical methods of Hargreaves and Ritchie. Next, the empirical models are calibrated using the ET ₀ values given by FAO-56 PM, and the estimates by the ANFIS and ANN techniques are compared with those of the calibrated models. Mean square error, mean absolute error, and determination coefficient statistics are used as comparison criteria for evaluation of performances of all the models considered. Based on these evaluations, it is found that the ANFIS and ANN schemes can be employed successfully in modeling the monthly mean ET ₀ , because both approaches yield better estimates than the classical methods, and yet ANFIS being slightly more successful than ANN.     

Fuzzy Genetic Approach for Estimating Reference Evapotranspiration of Turkey: Mediterranean Region

The applicability of fuzzy genetic (FG) approach in modeling reference evapotranspiration (ET₀) is investigated in this study. Daily solar radiation, air temperature, relative humidity and wind speed data of two stations, Isparta and Antalya, in Mediterranean region of Turkey, are used as inputs to the FG models to estimate ET₀ obtained using the FAO-56 Penman–Monteith equation. The FG estimates are compared with those of the artificial neural networks (ANN). Root mean-squared error, mean absolute error and determination coefficient statistics were used as comparison criteria for the evaluation of the models’ accuracies. It was found that the FG models generally performed better than the ANN models in modeling ET₀ of Mediterranean region of Turkey.     

Artificial Neural Network Rainfall-Discharge Model Assessment Under Rating Curve Uncertainty and Monthly Discharge Volume Predictions

The accuracy of rainfall-discharge volume model predictions depends on the model design and uncertainty of the available stage-discharge measurements used to fit the rating curve, which converts a time-series of recorded stage into discharge. In general, the rating curve uncertainty is the product of several combined sources. Over Algerian rivers, the extrapolation of the rating curve beyond the gauging range is the main source of this uncertainty. This study, therefore, represents a quantitative approach to reflect rigorously the impact of the rating curve uncertainty on the improvement of monthly discharge volume prediction quality by the artificial neural network (ANN) rainfall-discharge model. The rating curve uncertainty of the Fer à cheval hydrometric station in the Mazafran watershed is performed within Bayesian analysis for stationary rating curves using the BaRatin method. This allows as to build a new time series of discharge in order to assess an ANN rainfall-discharge model. To do that, Levenberg–Marquardt back propagation neuronal network has been applied over 1972-2012 time-period, for five hydrometric stations in the Algiers Coastal Basin. The model inputs were constructed in different ways, during the algorithm development, such as precipitation, antecedent precipitation with different monthly lag times and antecedent monthly discharge volume. The results indicate that training/validation of ANN rainfall-discharge volume model is widely affected by the streamflow datasets uncertainty. A large proportion of model prediction errors are significantly improved when considering the rating curve uncertainty.     

Comparison of M5 Model Tree and Artificial Neural Network’s Methodologies in Modelling Daily Reference Evapotranspiration from NOAA Satellite Images

The objective of this study was to compare feed-forward artificial neural network (ANN) and M5 model tree for estimating reference evapotranspiration (ET₀) only on the basis of the remote sensing based surface temperature (T_s) data. The input variables for these models were the daytime surface temperature at the cold pixel obtained from the AVHRR/NOAA sensor and extraterrestrial radiation (R_a). The study has been carried out in five irrigated units that cultivate sugar cane, which located in the Khuzestan plain in the southwest of Iran. A total of 663 images of NOAA–AVHRR level 1b during the period 1999–2009, covering the area of this study were collected from the Satellite Active Archive of NOAA. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two above approaches. The study demonstrated that modelling of ET₀ through the use of M5 model tree gave better estimates than the ANN technique. However, differences with the ANN model are small. Root mean square error and R² for the comparison between reference and estimated ET₀ for the tested data set using the proposed M5 model are 13.7 % and 0.96, respectively. For the ANN model these values are 14.3 % and 0.95, respectively.     

西藏高海拔地区气象数据缺失条件下的ET0计算研究

西藏高海拔地区低氧低压(平均不足海平面的2/3)、太阳辐射强(年太阳辐射6 000~8 000 MJ/m2)、近地层空气湿度变化大,加之西藏地区气象资料系列短、站点少,该地区ET_0计算具有特殊性及不便性。本研究基于西藏地区9个典型站点20年逐日气象资料,通过引入海拔因子与修正温度常数对Hargreaves(HS)模型进行改进,旨在得到一种少参、准确的高海拔地区ET_0简易计算方法。结果表明,海拔2 000 m以上地区Hargreaves-Elevation(HS-E)改进模型在不同时间尺度条件下的修正结果均明显优于HS模型且避免了原HS模型在高海拔地区ET_0计算出现负值的情况,提升了ET_0计算值的实用性与精度。以PM模型ET_0计算值为标准进行误差分析,HS-E模型逐日ET_0计算的纳什效率系数(NSE)、均方根误差(RMSE)和平均相对误差(MRE)分别为0.8、0.53mm/d和13.80%,逐月ET_0计算的NSE、RMSE和MRE分别为0.84、11.90 mm/month和12.50%;对比不同时间尺度条件下(日、月)误差分析结果可知,计算时间尺度越大HS-E模型结果越优。HS-E改进模型在高海拔地区适应性较强,具有较高的计算精度,可作为西藏海拔2000 m以上地区气象数据缺失条件下ET_0计算的推荐模型。     

Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment

This paper describes a detailed evaluation of the performance and characteristic behaviour of feed-forward artificial neural network (ANN) and M5 model tree for estimating reference evapotranspiration (ET₀) at four meteorological sites in an arid climate. The input variables for these models were the maximum and minimum air temperature, air humidity and extraterrestrial radiation. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that the ANN estimated ET₀ better than the M5 model tree but both models performed well for the study area and yielded results close to the FAO56-PM method. Root mean square error and R² for the comparison between reference and estimated ET₀ for the tested data using the proposed ANN model are 5.6 % and 0.98, respectively. For the M5 model tree method these values are 8.9 % and 0.98, respectively. The overall results are of significant practical use because the temperature and Humidity-based model can be used when radiation and wind speed data are not available.     

Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET₀). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET₀ was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET_0.     

Modeling Nonlinear Monthly Evapotranspiration Using Soft Computing and Data Reconstruction Techniques

The objective of this study is to develop soft computing and data reconstruction techniques for modeling monthly California Irrigation Management Information System (CIMIS) evapotranspiration (ET_o) at two stations, U.C. Riverside and Durham, in California. The nonlinear dynamics of monthly CIMIS ET_o is examined using autocorrelation function, phase space reconstruction, and close returns plot. The generalized regression neural networks and genetic algorithm (GRNN-GA) conjunction model is developed for modeling monthly CIMIS ET_o. Among different input variables considered, solar radiation (RAD) is found to be the most effective variable for modeling monthly CIMIS ET_o using GRNN-GA for both stations. Adding other input variables to the best 1-input combination improves the model performance. The generalized regression neural networks and backpropagation algorithm (GRNN-BP) conjunction model is compared with the results of GRNN-GA for modeling monthly CIMIS ET_o. Two bootstrap resampling methods are implemented to reconstruct the training data. Method 1 (1-BGRNN-GA) employs simple extensions of training data using the bootstrap resampling method. For each training data, method 2 (2-BGRNN-GA) uses individual bootstrap resampling of original training data. Results indicate that Method 2 (2-BGRNN-GA) improves modeling of monthly CIMIS ET_o and is more stable and reliable than are GRNN-GA, GRNN-BP, and Method 1 (1-BGRNN-GA).     

基于信息熵原理的东北地区参考作物蒸散量时空特征分析

根据1951—2013年东北地区116个气象站点的常规气象资料,基于信息熵理论构建了东北地区日参考作物蒸散量站点间的信息传输模型并分析了信息场的分布情况,利用聚类分析法解析了东北地区日参考作物蒸散量的区域相似性结构特征;并在此基础上选取6个代表性站点,运用重标极差法分析了参考作物蒸散量(ET_0)的时间分形特征。结果表明:空间上,东北地区日参考作物蒸散量的信息熵随纬度增加而减小,各站点信息传输指数随基站与辅站距离的增大而减小,且具有明显的各向异性;东北地区在站点群层面上的信息传输总体呈由南到北、由东到西的分布规律。时间上,东北地区多年平均ET_0总体呈下降趋势,哈尔滨、沈阳、赤峰、加格达奇、佳木斯、海拉尔6个代表性站点的ET_0在未来一定时段内的变化趋势趋于稳定且具有较强的持续性。     

The Effect of Temperature Adjustment on Reference Evapotranspiration and Reconnaissance Drought Index (RDI) in Iran

The reference evapotranspiration (ET₀) is necessary to calculate Reconnaissance Drought Index (RDI). To estimate ET₀, FAO56 Penman-Monteith method which needs reference stations data is commonly used. Most of the meteorological stations in Iran are classified as non-reference satations and The use of their data in ET₀ calculation can affect the RDI. The objective of the present study is to evaluate the effect of temperature adjustment based on the reference condition on ET₀ and RDI values in non-reference stations of Iran. For this purpose, the meteorological data, recorded during 1960–2014 in 27 non-reference stations located in arid and semi-arid regions, were used. First, the values of ET₀ were determined using observed values of temperature. Using these values, RDI were computed by Log-Normal and Gamma distributions at annual and 6-month scales. Then the values of minimum, maximum and dew point temperatures were adjusted on the basis of the reference condition. The values of ET₀ and consequently RDI were calculated using adjusted data. On the basis of obtained results, at annual and 6-month scales, using observed values of temperature instead of adjusted values in non-reference stations cause to overestimate the value of ET₀. Also, using observed data with no adjustment can change the drought class which was determined on the basis of RDI. According to these results, temperature adjustment based on reference condition can change the values of ET₀ and RDI which was calculated by using Log-Normal or Gamma distributions at annual and 6-month scales.     

基于粒子群算法与最小二乘支持向量机的ET0模拟

以月最高气温、月最低气温、月平均气温、平均风速、日照时数以及相对湿度6个气象因子的不同组合作为输入数据,以FAO Penman-Monteith公式计算结果作为标准值,构建基于粒子群优化算法与最小二乘支持向量机的ET_0预测模型(PSO-LSSVM)。选取新疆额尔齐斯河流域哈巴河气象站1986—2013年的气象数据进行模型训练与预测,并与其他常用ET_0计算公式进行对比研究。结果表明,PSO-LSSVM模型能够很好地反映ET_0同各气象因子之间的非线性关系,其中气温条件是影响ET_0模拟精度最重要的因素,同时随着气象因子输入的减少PSO-LSSVM模型模拟精度有所下降;当分别基于辐射条件、温度条件计算时,PSO-LSSVM模型模拟结果较Priestley-Taylor公式、Hargreaves-Samani公式计算结果要优。基于多因子量化指标的ET_0预测模型实现了精度和实用性的统一,可为缺资料地区ET_0研究预报提供科学参考。     

Evaluation of Reference Evapotranspiration Equations Under Humid Conditions

Five reference evapotranspiration (ET₀) equations are evaluated using data from seven humid locations. The equations evaluated include Hargreaves, Thornthwaite, Turc, Priestley–Taylor, and Jensen–Haise. The objective of this study is to evaluate ET₀ estimated by these equations against the corresponding values estimated using the standardized FAO-56 Penman–Monteith (PM) equation. For each location, ET₀ estimates by the all equations were statistically compared with FAO-56 PM ET₀ estimates. The Turc equation yielded the smallest root-mean-square-difference (RMSD) values at the all locations except Novi Sad, Serbia. The final ranking of equations was based on the weighted RMSD. The Turc equation has the lowest weighted RMSD and ranking first, and other equations ranked in decreasing order are: Priestley–Taylor, Jensen–Haise, Thornthwaite, and Hargreaves. The Turc equation gives the reliable calculation at all humid locations and it has proven to be the most adjustable to the local climatic conditions. The results obtained from this study, indicate very clearly that the Turc equation is most suitable for estimating reference evapotranspiration at humid locations when weather data are insufficient to apply the FAO-56 PM equation.     

Development of Nonlinear Model Based on Wavelet-ANFIS for Rainfall Forecasting at Klang Gates Dam

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. Artificial neural networks (ANN) have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models. However, time series prediction based on ANN algorithms is fundamentally difficult and faces some other problems. For this purpose, one method that has been identified as a possible alternative for ANN in hydrology and water resources problems is the adaptive neuro-fuzzy inference system (ANFIS). Nevertheless, the data arising from the monitoring stations and experiment might be corrupted by noise signals owing to systematic and non-systematic errors. This noisy data often made the prediction task relatively difficult. Thus, in order to compensate for this augmented noise, the primary objective of this paper is to develop a technique that could enhance the accuracy of rainfall prediction. Therefore, the wavelet decomposition method is proposed to link to ANFIS and ANN models. In this paper, two scenarios are employed; in the first scenario, monthly rainfall value is imposed solely as an input in different time delays from the time (t) to the time (t-4) into ANN and ANFIS, second scenario uses the wavelet transform to eliminate the error and prepares sub-series as inputs in different time delays to the ANN and ANFIS. The four criteria as Root Mean Square Error (RMSE), Correlation Coefficient (R ²), Gamma coefficient (G), and Spearman Correlation Coefficient (ρ) are used to evaluate the proposed models. The results showed that the model based on wavelet decomposition conjoined with ANFIS could perform better than the ANN and ANFIS models individually.     

Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform

Precipitation prediction is of dispensable importance in many hydrological applications. In this study, monthly precipitation data sets from Serbia for the period 1946–2012 were used to estimate precipitation. To fulfil this objective, three mathematical techniques named artificial neural network (ANN), genetic programming (GP) and support vector machine with wavelet transform algorithm (WT-SVM) were applied. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R²) were used to evaluate the performance of the WT-SVM, GP and ANN models. The achieved results demonstrate that the WT-SVM outperforms the GP and ANN models for estimating monthly precipitation.     

Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure

Potential evapotranspiration (ET_o) is an essential hydrologic parameter for having better understanding for hydrologic cycle in certain catchment area. In addition, ET_o is vital for calculating the agricultural demand. In fact, Penman-Monteith (PM) method is considered as reference method for estimating (ET_o), however, this method required a lot of data to be used which is not usually available in many catchment areas. Furthermore, there are several efforts that have been performed as competitor to reach accurate estimation of (ET_o) when there is lack of data to utilize (PM) method, but still required numerous research. Recently, methods based on Artificial Intelligence (AI) have been suggested to provide reliable prediction model for several application in engineering and especially for hydrological process. However, time series prediction based on Artificial Neural Network (ANN) learning algorithms is fundamentally difficult and faces problem. One of the major shortcomings is that the ANN model experiences over-fitting problem during training session and also occurs when a neural network loses its generalization. In this research a modification for the classical Multi Layer Preceptron- Artificial Neural Network (MLP-ANN) modeling namely; Ensemble Neural Network (ENN) is proposed and applied for predicting daily ET_o. The proposed model applied at two different region with two different climatic conditions, Rasht city located north part of Iran and Johor Bahru City, Johor, Malaysia using maximum and minimum daily temperature collected from 1975 to 2005. The result showed that the ENN outperformed the classical MLP-ANN method and successfully predict daily ETo utilizing maximum and minimum temperature only with satisfactory level of accuracy. In addition, the proposed model could achieve accuracy level better than the traditional competitor methods for ET_o.     

Evaluation of Reference Crop Evapotranspiration Equations in Various Climates

Evapotranspiration is one of the most important elements for quantifying available water since it generally constitutes the largest component of the terrestrial water cycle. This study evaluated four models (Makkink, Turc, Priestley–Taylor and Hargreaves) commonly used to estimate monthly reference crop evapotranspiration (ET_o) values. The main aim of this study was to determine the model used to estimate ET_o with small data requirements and high accuracy for twelve synoptic stations in four climates of Iran. The results showed that the Turc model was the best suited model in estimating ET_o for cold humid and arid climates. The Hargreaves model turned out to be the most precise model under warm humid and semi-arid climatic conditions. In contrast, the Makkink model presented the poorest estimates in all of the climates exception for cold humid environment. In cold humid climate, the Hargreaves model was the least accurate model in estimating ET_o. In general, the results obtained from this study revealed very clearly that the Makkink and Priestley–Taylor models estimated ET_o values less accurately than Turc and Hargreaves models for the all climates.     

Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks

This study is an attempt to find best alternative method to estimate reference evapotranspiration (ET_o) for the Mahanadi reservoir project (MRP) command area located at Raipur (Chhattisgarh) in India, when input climatic parameters are insufficient to apply standard Food and Agriculture Organization (FAO) of the United Nations Penman–Monteith (P–M) method. To identify the best alternative climatic based method that yield results closest to the P–M method, performances of four climate based methods namely Blaney–Criddle, Radiation, Modified Penman and Pan evaporation were compared with the FAO-56 Penman–Monteith method. Performances were evaluated using the statistical indices. The statistical indices used in the analysis were the standard error of estimate (SEE), raw standard error of estimate (RSEE) and the model efficiency. Study was extended to identify the ability of Artificial Neural Networks (ANNs) for estimation of ET_o in comparison to climatic based methods. The networks, using varied input combinations of climatic variables have been trained using the backpropagation with variable learning rate training algorithm. ANN models were performed better than the climatic based methods in all performance indices. The analyses of results of ANN model suggest that the ET_o can be estimated from maximum and minimum temperature using ANN approach in MPR area.     

Framework for Standardizing Less Data-Intensive Methods of Reference Evapotranspiration Estimation

Evapotranspiration is one of the vital components of water cycle and its accurate estimation is the key to sustainable management of irrigation water. The FAO Penman-Monteith (FAO-PM) method is recommended as the standard method for computing reference evapotranspiration (ET_o) as well as for evaluating other indirect methods. However, due to the lack of weather data such as radiation, relative humidity and wind speed in many regions of the world, especially in developing countries, the FAO-PM method is difficult to use. To address this issue, a fairly robust methodology is proposed in this study to standardize two popular less data-intensive (temperature-based) ET_o methods, viz., Hargreaves-Samani (HS) and Penman-Monteith Temperature (PMT) against the FAO-PM method. To achieve this goal, the daily and monthly biases of these two methods were adjusted using the weather data of 14 locations for the 1979–2003 period. Subsequently, the performance of the standardized (de-biased) less data-intensive methods were verified using salient statistical and graphical indicators for the 2004–2013 period. The results indicated that the HS and PMT methods underestimate ET_o on a monthly time step by 9.62 and 14.77%, respectively. However, the performances of these methods significantly improve after the standardization. The estimates of ET_o by the standardized less data-intensive methods were found to be in close agreement with those by the standard FAO-PM method, thereby suggesting the usefulness and applicability of the proposed framework in data-scarce situations irrespective of agro-climatic conditions.     

考虑气象因子不确定性的参考作物蒸散量预报方法

参考作物蒸散量(ET0)的准确预测预报对于制定作物灌溉制度与实时灌溉调度具有重要意义,然而气象因子的不确定性极大的影响着ET0的预测精度.因此本研究采用马尔科夫蒙特卡罗模拟与自适应采样算法相结合的方法(AM-MCMC)对气象因子的不确定性进行修正,以气象站实测ET0作为标准值,利用径向基神经网络(RBF)模型建立气象因...     

An Improved Estimation of the Angstrom–Prescott Radiation Coefficients for the FAO56 Penman–Monteith Evapotranspiration Method

The FAO56 Penman–Monteith (FAO56-PM) method is known as the standard method for estimating reference evapotranspiration (ET₀) in a variety of climate types. Global solar radiation (R_s) is one of the essential inputs of this model, which is usually estimated from the Angstrom–Prescott (AP) method. The major drawback of the FAO56 pre-defined AP coefficients application is that the AP coefficients might need local calibration, to estimate ET₀ accurately. The aim of this study is to compare the effect of the FAO56 pre-defined AP coefficients (i.e. a and b) and the locally calibrated ones, on estimating daily ET₀ in 15 sites over Iran. Using long-term (1980–2007) experimental global solar radiation data (R_s), new locally calibrated (a) and (b) coefficients are suggested and new ET₀ values are determined accordingly. It was found that the range of the calibrated AP coefficients (a, b) are climate dependent and locally different from those of recommended by the FAO56-PM method. Estimated ET₀ at daily scale, improved up to 72.7 % when the calibrated AP coefficients were applied instead of FAO56 pre-defined AP coefficients. Based on the results, applying the FAO56 pre-defined AP coefficients (i.e. a?=?0.25 and b?=?0.50) in northern subtropical-humid and southern hot climates caused larger ET₀ errors. By contrast, the least ET₀ errors were found in cool arid and cool semi-arid inland climates, locating about 1,330 above sea level. The correlations between the calibrated AP coefficients and geographical factors are also discussed in this research.