Estimation of daily pan evaporation using neural networks and meta-heuristic approaches

Ashrafzadeh, Afshin; Malik, Anurag; Jothiprakash, V.; Ghorbani, Mohammad Ali; Biazar, Seyed Mostafa

doi:10.1080/09715010.2018.1498754

Cited by 36 publications

(29 citation statements)

References 30 publications

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“…Thus, selecting the most appropriate variable for reliable EP estimation is a difficult task. The practice of the GT (Gamma test) for the selection of best inputs for ML models has received extensive attention in recent years (Ashrafzadeh et al, 2020;Das et al, 2019;Malik et al, 2018Malik et al, , 2020aMalik et al, , 2020cMohammadi et al, 2018;Singh et al, 2018). Rashidi et al (2016) forecasted daily suspended sediment load (SSL) by optimizing the SVM (support vector machine) model with two kernel functions i.e.…”

Section: Introductionmentioning

confidence: 99%

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Malik

Tikhamarine

Al‐Ansari

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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Section: Introductionmentioning

confidence: 99%

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Malik

Tikhamarine

Al‐Ansari

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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“…Despite this, evaporation is less well known as a component of the hydrological cycle (Jing et al 2019). Therefore, it is important to accurately predict the rate of evaporation, especially in arid and semi-arid regions, which has a critical impact on agricultural issues and water resources management (Ashrafzadeh et al 2020;Ghorbani et al 2018a). Evaporation is important in arid and semi-arid regions because the level of evaporation is higher compared to other elements of the hydrological cycle such as precipitation and groundwater flow.…”

Section: Introductionmentioning

confidence: 99%

Solving the pan evaporation process complexity using the development of multiple mode of neurocomputing models

Ghorbani

Jabehdar

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2021

Theor Appl Climatol

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Finding an accurate computational method for predicting pan evaporation (EP), can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called Multiple Model-Support Vector Machine (MM-SVM) with the aim of increasing the accuracy of EP prediction on a monthly scale (EPm) in two meteorological stations (Ardabil and Khalkhal) using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)) were evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE) and coefficient of determination (R 2 )) and with the help of case visual indicators Were compared. According to the results of evaluation indicators in the test phase, two models MM-SVM-6 and ANN-5 with (RMSE, MAE, KGE and R 2 equal to 1.088, 0.761, 0.79, 0.54 mm. month -1 , 0.819, 0.903 and 0.939, 0.962) and with three input variables, were introduced as the top models in Ardabil and Khalkhal stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs predicted by independent models, its power to predict EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant and decreasing modes in EPm prediction accuracy by this hybrid model under the above conditions (especially in Ardabil station) were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.

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“…In fact, the AI models can predict the drought events that do not have a good and straightforward mathematical solution and were proven to have the ability to capture the white noise, nonstationary, and nonlinearity in the time series [18]. Multilayer Perceptron (MLP) neural network is the most famous type of AIs which has been widely used in hydrological and meteorological modeling studies [19][20][21][22][23][24][25][26][27][28][29][30]. Malik and Kumar [31] used the MLP model for meteorological drought prediction based on Effective Drought Index (EDI) in the Uttarakhand state of India and reported the acceptable performance of this model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)

Aghelpour

Varshavian

2021

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Precipitation deficit causes meteorological drought, and its continuation appears as other different types of droughts including hydrological, agricultural, economic, and social droughts. Multivariate Standardized Precipitation Index (MSPI) can show the drought status from the perspective of different drought types simultaneously. Forecasting multivariate droughts can provide good information about the future status of a region and will be applicable for the planners of different water divisions. In this study, the MLP model and its hybrid form with the Imperialistic Competitive Algorithm (MLP-ICA) have been investigated for the first time in multivariate drought studies. For this purpose, two semi-arid stations of western Iran were selected, and their precipitation data were provided from the Iranian Meteorological Organization (IRIMO), during the period of 1988–2017. MSPI was calculated in 5-time windows of the multivariate drought, including MSPI3–6 (drought in perspectives of soil moisture and surface hydrology simultaneously), MSPI6–12 (hydrological and agricultural droughts simultaneously), MSPI3–12 (soil moisture, surface hydrology, and agricultural droughts simultaneously), MSPI12–24 (drought in perspectives of agriculture and groundwater simultaneously), and MSPI24–48 (socio-economical droughts). The results showed acceptable performances in forecasting multivariate droughts. In both stations, the larger time windows (MSPI12–24 and MSPI24–48) had better predictions than the smaller ones (MSPI3–6, MSPI6–12, and MSPI3–12). Generally, it can be reported that, by decreasing the size of the time window, the gradual changes of the index give way to sudden jumps. This causes weaker autocorrelation and consequently weaker predictions, e.g., forecasting droughts from the perspective of soil moisture and surface hydrology simultaneously (MSPI3–6). The hybrid MLP-ICA shows stronger prediction results than the simple MLP model in all comparisons. The ICA optimizer could averagely improve MLP’s accuracy by 28.5%, which is a significant improvement. According to the evaluations (RMSE = 0.20; MAE = 0.15; R = 0.95), the results are hopeful for simultaneous forecasting of different drought types and can be tested for other similar areas.

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Estimation of daily pan evaporation using neural networks and meta-heuristic approaches

Cited by 36 publications

References 30 publications

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

Solving the pan evaporation process complexity using the development of multiple mode of neurocomputing models

Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)

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