Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm

Maroufpoor, Saman; Bozorg‐Haddad, Omid; Maroufpoor, Eisa

doi:10.1016/j.jhydrol.2020.125060

Cited by 83 publications

(31 citation statements)

References 73 publications

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“…Optimal selection of parameters in the structure of ANN will increase its performance. The weight of each neuron is one of the main factors in the ANN structure (Maroufpoor et al ., 2020), which in this study was adjusted by a genetic algorithm. The population size used was 80, mutation parameter rate was 0.001, the crossover used was 0.6, and number of generations was 1600, and achieved high performance and accuracy according to DeJong (1975), Grefenstette (1986), Schlierkamp‐Voosen (1993), Chiroma et al .…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence approach to estimating rice yield*

Babaee

Maroufpoor

Jalali

et al. 2021

Irrigation and Drainage

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After wheat, rice is one of the most important agricultural products in the world, and Iran has a special position here with annual production of more than 2 million t of rice. Evaluation of crop yield has an important role in agricultural policy making due to different conditions and restrictions. Estimating rice yield is a key factor in food security. Any change in the effective parameters can cause changes in rice yield and therefore the food security of the population will be affected. In this study, rice crop yield was estimated by artificial neural networks (ANNs) and ANN‐genetic programming (GP) in 2011 and 2015. Rainfall, permeability, soil texture, land type, evapotranspiration and inlet and inflow and outflow water to paddy lands were used as inputs. The results showed that the ANN‐GP with a root mean square error (RMSE = 80.8 kg ha‾¹) and a correlation coefficient (CC = 0.91) was more accurate than the stand‐alone ANN (with RMSE = 139 kg ha‾¹ and CC = 0.67). Finally, the effect of each input parameter on rice yield was evaluated. Irrigation, drainage and soil type parameters had the best impact rank, with 36, 28 and 31%, respectively. Therefore, the proposed method can act as an efficient tool in estimating rice yield and help decision makers to manage and develop the agricultural system.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence approach to estimating rice yield*

Babaee

Maroufpoor

Jalali

et al. 2021

Irrigation and Drainage

Self Cite

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“…Moreover, the proposed model is compared with the existing variants of SVR and showed that the performance of the SVR-GWO gives occasionally competitive and very promising results. Maroufpoorb et al [ 34 ] proposed the concept of hybrid Artificial Neural Network-Gray Wolf Optimization (ANN-GWO) model and predicted the ET for Iran.…”

Section: Literature Of Irrigation Schedulingmentioning

confidence: 99%

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

Saggi

Jain

2022

Arch Computat Methods Eng

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From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big data analytics becomes a key technology to perform analysis of voluminous data. Irrigation water management is a challenging task for sustainable agriculture. It depends on various parameters related to climate, soil and weather conditions. For accurate estimation of requirement of water for a crop a strong modeling is required. This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation.

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“…The following five evaluation indexes (Maroufpoor, Bozorg-Haddad and Maroufpoor 2020;Nash and Sutcliffe, 1970) were used to evaluate the performance of the model.…”

Section: Model Performance Evaluationmentioning

confidence: 99%

“…It is mainly used to reflect the distribution characteristics of original data and can also compare the distribution characteristics of multiple groups of data. The boxplot is displayed based on an error distribution of four values (Maroufpoor et al 2020Seyedzadeh et al 2020 which are the first quartile (Q1), the third quartile (Q3), the interquartile range (IQR), and the portion of the rectangle showing the median. This is shown in Figure 8.…”

Section: Comparison Of the Modelsmentioning

confidence: 99%

Estimation and Comparative Analysis of Daily Reference Evapotranspiration Based on Different Input Combinations and Different Models

Niu

Li²,

Liu³

et al. 2021

Preprint

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Daily reference evapotranspiration (ET0) is the most crucial link in estimating crop water demand. In this study, Levenberg-Marquardt (L-M), Genetic Algorithm-Back Propagation (GA-BP) and Partial Least Squares Regression (PLSR) models were introduced to calculate the ET0 values, Based on the Pearson Correlation analysis method, five meteorological factors were obtained, which were combined into six different input scenarios. Compared with the values that calculated by the the Penman Monteith (PM) formula. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) were used to evaluate the simulation performance of the models. The results showed that the simulation effect of the L-M model is better than that of the GA-BP model and PLSR model in all scenarios. PLSR model has the worst performance. The SI index of L-M6 was 46.69% lower than that of GA-BP6 and 65.78% lower than that of PLSR6. When the input factors are 3, the simulation effect of the input wind speed, the maximum temperature and the minimum temperature is the best. L-M model and GA-BP model can predict the ET0 in the region with a lack of meteorological data. This study provides an important reference for high-precision prediction of ET0 under different input combinations of meteorological factors.

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Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm

Cited by 83 publications

References 73 publications

Artificial intelligence approach to estimating rice yield*

Artificial intelligence approach to estimating rice yield*

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

Estimation and Comparative Analysis of Daily Reference Evapotranspiration Based on Different Input Combinations and Different Models

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