Daily scale evapotranspiration prediction over the coastal region of southwest Bangladesh: new development of artificial intelligence model

Lü, Ye; Zahra, Musaddak Maher Abdul; Al-Bedyry, Najah Kadhim; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1007/s00477-021-02055-4

Cited by 18 publications

(7 citation statements)

References 75 publications

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“…Also applying the long-range prediction of other meteorological variables, such as solar radiation and others as a feature to predict AT, is the necessity of the research. Worth to mention, DENFIS and HyFIS models were modelled over the literature for different hydrometrological parameters and confirmed their feasibility such as pan evaporation [43], rainfall [44,45], evapotranspiration [46], land surface temperature [47], crops suitability [48], and energy consumption [49].…”

Section: Research Motivationmentioning

confidence: 75%

“…Recently, [81] reported the ANFIS and DENFIS with a marginal di erence using short-ranged data (of soil moisture) compared with the current study using long-ranged data. e authors also mentioned that ANFIS (step size: 0.001, membership type: Gaussian) and DENFIS (Max (0.85-0.94) against the counterpart MARS to predict the daily scale evapotranspiration for three different coastal locations [46].…”

Section: Discussion and Comparative Analysismentioning

confidence: 99%

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Establishment of Dynamic Evolving Neural-Fuzzy Inference System Model for Natural Air Temperature Prediction

et al. 2022

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Air temperature (AT) prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system (DENFIS), hybrid neural-fuzzy inference system (HyFIS), and adaptive neurofuzzy inference system (ANFIS) for AT prediction. Modelling was done for three stations in North Dakota (ND), USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when handling highly variable data, such as AT, which shows a high positive correlation with average daily dew point (DP), total solar radiation (TSR), and negative correlation with average wind speed (WS). At the Robinson station, DENFIS performed the best with a coefficient of determination (R2) of 0.96 and a modified index of agreement (md) of 0.92, followed by ANFIS with R2 of 0.94 and md of 0.89, and HyFIS with R2 of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R2: 0.953/0.960, md: 0.903/0.912, then ANFIS with R2: 0.943/0.942, md: 0.888/0.890, and HyFIS with R2: 0.908/0.905, md: 0.845/0.821, respectively. It can be concluded that all three models are capable of predicting AT with high efficiency by only using DP, TSR, and WS as input variables. This makes the application of these models more reliable for a meteorological variable with the need for the least number of input variables. The study can be valuable for the areas where the climatological and seasonal variations are studied and will allow providing excellent prediction results with the least error margin and without a huge expenditure.

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Section: Research Motivationmentioning

confidence: 75%

Section: Discussion and Comparative Analysismentioning

confidence: 99%

Establishment of Dynamic Evolving Neural-Fuzzy Inference System Model for Natural Air Temperature Prediction

et al. 2022

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“…Evapotranspiration is a complex hydrological process that depends on the integrated effect of several climatic variables [69]. It also governs the soil moisture, surface runoff, plant growth, and groundwater recharge for optimizing the available water resources [70].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

et al. 2022

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Reference evapotranspiration (ETo) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ETo is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ETo at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (Rs), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evaluated based on five statistical indicators i.e., CC (correlation coefficient), WI (Willmott index), EC (efficiency coefficient), RMSE (root mean square error), and MAE (mean absolute error) presented through a heatmap along with graphical interpretation (Taylor diagram, time-series, and scatter plots). The results showed that the SVM-1 model corresponding to the C-1 input combination outperformed the other ML and empirical models at both stations. Moreover, the SVM-1 model had the lowest MAE (0.076, 0.047 mm/month) and RMSE (0.110, 0.063 mm/month), and highest EC (0.995, 0.999), CC (0.998, 0.999), and WI (0.999, 1.000) values during validation period at Nagina and Pantnagar stations, respectively, and closely followed by the M5P model. Consequently, the ML model (i.e., SVM) was found to be more robust, and reliable in monthly ETo estimation and can be used as a promising alternative to empirical models at both study locations.

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“…DENFIS uses an evolving clustering algorithm that takes into account the greatest possible distance between the points and the centre of the cluster. Thus, it can handle the noise in the data (Ye et al ., 2022). Furthermore, DENFIS learning approach assesses the position of the input vectors in the feature space assessed by fuzzy neural networks and then a dynamic process forms a fuzzy inference system to predict the output based on the nearest fuzzy rules.…”

Section: Methodsmentioning

confidence: 99%

High‐resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models

Sobh

Nashwan

Amer

2022

Intl Journal of Climatology

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Accurate estimation of evapotranspiration has crucial importance in arid regions like Egypt, which suffers from the scarcity of precipitation and water shortages. This study provides an investigation of the performance of 31 widely used empirical equations and 20 models developed using five artificial intelligence (AI) algorithms to estimate reference evapotranspiration (ETo) to generate gridded high-resolution daily ETo estimates over Egypt. The AI algorithms include support vector machine-radial basis function (SVM-RBF), random forest (RF), group method of data handling neural network (GMDH-NN), multivariate adaptive regression splines (MARS), as well as Dynamic Evolving Neural Fuzzy Interference System (DENFIS). Daily observations records of 41 stations distributed over Egypt were used to calculate ETo using FAO56 Penman-Monteith equation as a reference estimate. The multi-parameter Kling-Gupta efficiency (KGE) metric was used as an evaluation metric for its robustness in representing different statistical error/agreement characteristics in a single value. By category, the empirical equations based on radiation performed better in replication FAO56-PM followed by temperature-and mass-transfer-based ones. Ritchie equation was found to be the best overall in Egypt (median KGE 0.75) followed by Caprio (median KGE 0.65), and Penman (median KGE 0.52) equations based on station-wise ranking. On the other hand, the RF model, having maximum and minimum temperatures, wind speed, and relative humidity as predictors, outperformed other AI algorithms. The generated 0.10°×0.10° daily estimates of ETo enabled the detection of a significant increase of 0.12-0.16 mm/decade in the agricultural-dependent Nile Delta using the modified Mann Kendall test and Sen's slope estimator.

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Daily scale evapotranspiration prediction over the coastal region of southwest Bangladesh: new development of artificial intelligence model

Cited by 18 publications

References 75 publications

Establishment of Dynamic Evolving Neural-Fuzzy Inference System Model for Natural Air Temperature Prediction

Establishment of Dynamic Evolving Neural-Fuzzy Inference System Model for Natural Air Temperature Prediction

Evaluation of Machine Learning versus Empirical Models for Monthly Reference Evapotranspiration Estimation in Uttar Pradesh and Uttarakhand States, India

High‐resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models

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