Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data

Elbeltagi, Ahmed; Kumar, Navsal; Chandel, Abhishish; Arshad, Arfan; Pande, Chaitanya B.; Islam, Abu Reza Md. Towfiqul

doi:10.1007/s10661-022-09812-0

Cited by 27 publications

(5 citation statements)

References 39 publications

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“…This study investigated different activation functions of the MLP model for pan evaporation estimation in a semi-arid region. Also, it is recommended to use different activation functions of the MLP model for hydrological modeling by MLP model, such as actual evaporation [57,62], rainfall [63], runoff [64], solar radiation [65], snow cov-er area [66], soil temperature [67], soil pore-water pressure [68] simulation. In some studies, the performance of different learning algorithms for training the MLP model was evaluated [63,68,69] for modeling different hydrological variables.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

Aghelpour

Bagheri-Khalili

Varshavian

et al. 2022

Water

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Evaporation is one of the main components of the hydrological cycle, and its estimation is crucial and important for water resources management issues. Access to a reliable estimator tool for evaporation simulation is important in arid and semi-arid areas such as Iran, which lose more than 70% of their received precipitation by evaporation. Current research employs the Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms for training the Multilayer Perceptron (MLP) model (as MLP-BR and MLP-SCG) and comparing their performance with the Levenberg–Marquardt (LM) algorithm (as MLP-LM). For this purpose, 16 meteorological variables were used on a daily scale; including temperature (5 variables), air pressure (4 variables), and relative humidity (6 variables) as input data sets, and pan evaporation as the target variable of the MLP model. The surveys were conducted during the period of 2006–2021 in Fars Province in Iran, which is a semi-arid region and has many natural lakes. Various combinations of input-target pairs were tested by several learning algorithms, resulting in seven input scenarios: (1) temperature-based (T), (2) pressure-based (F), (3) humidity-based (RH), (4) temperature–pressure-based (T-F), (5) temperature–humidity-based (T-RH), (6) pressure–humidity-based (F-RH) and (7) temperature–pressure–humidity-based (T-F-RH). The results indicated the relative superiority of the three-component scenario of T-F-RH, and a considerable weakness in the single-component scenario of RH compared with others. The best performance with a root mean square error (RMSE) equal to 1.629 and 1.742 mm per day and a Wilmott Index (WI) equal to 0.957 and 0.949 (respectively for validation and test periods) belonged to the MLP-BR model. Additionally, the amount of R2 (greater than 84%), Nash-Sutcliff efficiency (greater than 0.8) and normalized RMSE (less than 0.1) all indicate the reliability of the estimates provided for the daily pan evaporation. In the comparison between the studied training algorithms, two algorithms, BR and SCG, in most cases, showed better performance than the powerful and common LM algorithm. The obtained results suggest that future researchers in this field consider BR and SCG training algorithms for the supervised training of MLP for the numerical estimation of pan evaporation by the MLP model.

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Section: Discussionmentioning

confidence: 99%

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

Aghelpour

Bagheri-Khalili

Varshavian

et al. 2022

Water

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“…The artificial neural network (ANN) is a data-driven artificial intelligence technique which utilizes the learning experience while training a dataset to make predictions on a new dataset (Elbeltagi et al 2022).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of artificial intelligence-based modelling for the prediction of crop water stress index

Kumar,

Shankar

2024

Preprint

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The study evaluates the performance of three artificial intelligence (AI) techniques viz. support vector regression (SVR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for predicting the crop water stress index (CWSI) using relative humidity, air temperature, and canopy temperature. Field crop experiments were conducted on Wheat (during 2018, 2019) and Indian mustard (during 2017, 2018) to observe the canopy temperature in different irrigation levels. The experimentally obtained empirical CWSI was considered as the reference CWSI. Different configurations of ANN, SVR and ANFIS models were developed and validated with the empirical CWSI. The most optimal model structures for predicting CWSI were ANN5 (ANN with 5 hidden neurons), SVRQ (SVR with Quadratic kernel) and ANFIS2 (ANFIS with 2 membership functions) in Wheat; and ANN3 (ANN with 3 hidden neurons), SVRQ and ANFIS2 in Indian mustard. Based on the values of error statistics during validation, all three models presented a satisfactory performance, however, the efficacy of the models was relatively better in the case of Wheat. The model predictions at low CWSI values indicate deviations in the case of both crops. Overall, the study results indicate that data-driven-based AI techniques can be used as potential and reliable alternatives for predicting CWSI since the performance of the models is reliable for CWSI values commonly encountered in irrigation scheduling.

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“…Recent studies have employed climate-crop modeling approaches to project changes in CWR under future climate scenarios (Elliott et al, 2014;Chen et al, 2018;Liu et al, 2018;Elbeltagi et al, 2022). The dual crop coefficient approach, as described by the Food and Agriculture Organization (FAO) in their Irrigation and Drainage Paper No.…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of future climate scenarios on crop water requirements and agricultural water supply across different climatic zones of Pakistan

Shafeeque,

Bibi

2023

Front. Earth Sci.

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This study investigates the potential impacts of future climate change on crop water requirements (CWR) in different climatic zones of Pakistan and the subsequent implications for agricultural water demand and supply. Using the latest CMIP6 climate projections, we focused on Rabi and Kharif crop seasons, which are crucial for the growth of major crops in Pakistan. An empirical climate-crop and a hydrological model based on the Budyko theory were modified and forced to project future CWR changes and the potential widening of the water demand-supply gap until 2,100. Our results indicate a significant rise in mean annual CWR across all zones and emissions scenarios, with increasing rates at 2.30–2.57 mm/yr under SSP585 and 1.0–1.26 mm/yr under SSP245. Both Rabi and Kharif seasons show rising CWR, notably more under SSP585 (Kharif: 8%–14%, Rabi: 12%–15%) than SSP245 (Kharif: 4%–7%, Rabi: 6%–8%). The demand-supply gap is expected to grow notably, with arid and semi-arid zones being the most affected. Compared to 2015–2025, by 2091–2,100, the gap increased by 7%–15% (SSP245) and 15%–28% (SSP585) for Kharif and 7%–13% (SSP245) and 13%–32% (SSP585) for Rabi. To address these challenges, we recommend strategies like enhancing irrigation efficiency, adjusting crop patterns, and developing heat-resilient crops. Our insights aim to inform policy decisions on agriculture and water management in Pakistan under future climate change.

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Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data

Cited by 27 publications

References 39 publications

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region

Application of artificial intelligence-based modelling for the prediction of crop water stress index

Assessing the impact of future climate scenarios on crop water requirements and agricultural water supply across different climatic zones of Pakistan

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