Improved prediction of daily pan evaporation using Deep-LSTM model

Majhi, Babita; Naidu, Diwakar; Mishra, Ambika Prasad; Satapathy, Suresh Chandra

doi:10.1007/s00521-019-04127-7

Cited by 84 publications

(50 citation statements)

References 52 publications

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“…In recent years, a number of investigations into the implementation of machine learning (ML) models for evaporation estimation have been conducted across different regions (Abghari, Ahmadi, Besharat, & Rezaverdinejad, 2012;Baydaroǧlu & Koçak, 2014;Di et al, 2019;Fallah-Mehdipour, Bozorg Haddad, & Mariño, 2013;Fotovatikhah, Herrera, Shamshirband, Ardabili, & Piran, 2018;Lu et al, 2018;Majhi, Naidu, Mishra, & Satapathy, 2019;Moazenzadeh et al, 2018;Tabari, Marofi, & Sabziparvar, 2010). Several versions of ML models have been developed for evaporation modeling, including evolutionary computing, classical neural networks, kernel models, fuzzy logic, decision trees, deep learning, complementary wavelet-machine learning, and hybrid machine learning, among others (Danandeh Mehr et al, 2018;Fahimi, Yaseen, & El-shafie, 2016;Jing et al, 2019;Yaseen, Sulaiman, Deo, & Chau, 2019).…”

Section: Introductionmentioning

confidence: 99%

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation-the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM)were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R 2 = .92), and with all variables as inputs at Station II (R 2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.

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Section: Introductionmentioning

confidence: 99%

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Al-Juboori

Beyaztaş

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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“…The results of this study were compared with the previous studies conducted on modeling pan-evaporation using several artificial intelligence (AI) techniques optimized by bio-inspired algorithms (Kumar et al, 2021;Majhi et al, 2020;Qasem et al, 2019;Salih et al, 2019;Singh et al, 2021). The previous studies reported the effective utility of hybrid AI methods for pan-evaporation at different locations in varying climates through statistical metrics and visual investigation.…”

Section: Discussionmentioning

confidence: 98%

“…They found a better performance of the hybrid SVR-RSM model over other models. Allawi et al (2020), Patle et al (2020), Majhi et al (2020), and Sebbar et al (2019) applied various SCT (soft computing techniques) in different regions for predicting panevaporation. Their results endorse the feasibility of SCT.…”

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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“…Considering the collected environmental data had different magnitudes, it was necessary to first normalize the data. In this paper, the linear function normalization method [24] (min-max scaling) was applied to convert the data to the range [0, 1]. The equation is shown in Equation (1).…”

Section: Data Preprocessing and Correlation Analysismentioning

confidence: 99%

Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

et al. 2021

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In order to create an irrigation scheduling plan for use in large-area citrus orchards, an environmental information collection system of citrus orchards was established based on the Internet of Things (IoT). With the environmental information data, deep bidirectional long short-term memory (Bid-LSTM) networks are proposed to improve soil moisture (SM) and soil electrical conductivity (SEC) predictions, providing a meaningful reference for the irrigation and fertilization of citrus orchards. The IoT system contains SM, SEC, air temperature and humidity, wind speed, and precipitation sensors, while the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were calculated to evaluate the performance of the models. The performance of the deep Bid-LSTM model was compared with a multi-layer neural network (MLNN). The results for the performance criteria reveal that the proposed deep Bid-LSTM networks perform better than the MLNN model, according to many of the evaluation indicators of this study.

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Improved prediction of daily pan evaporation using Deep-LSTM model

Cited by 84 publications

References 52 publications

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

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

Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

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