Prediction of daily reference crop evapotranspiration in different Chinese climate zones: Combined application of key meteorological factors and Elman algorithm

Zhao, Long; Zhao, Xinbo; Pan, Xiaolong; Shi, Yi; Qiu, Zhaobin; Li, Xiuzhen; Xing, Xuguang; Bai, Jiayi

doi:10.1016/j.jhydrol.2022.127822

Cited by 16 publications

(3 citation statements)

References 27 publications

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“…The Elman model can be described as Eq (18). Where, y is the node vector of the output layer; x is the node vector of the middle layer; u is the input vector; x c is the feedback state vector; ω 1 is connection weight from hidden layer to undertake layer.…”

Section: Elman Neural Networkmentioning

confidence: 99%

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Improved GWO and its application in parameter optimization of Elman neural network

et al. 2023

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Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. SGWO was applied to optimize Elman network structure, and a new prediction method (SGWO-Elman) was proposed. The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.

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Section: Elman Neural Networkmentioning

confidence: 99%

“…The experimental results proved that WOA-Elman has good engineering utility the porosity prediction. In addition, WOA-Elman also played an important role in weather prediction [18] and landslide probability prediction [19].…”

Section: Introductionmentioning

confidence: 99%

Improved GWO and its application in parameter optimization of Elman neural network

et al. 2023

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“…Agricultural water includes water for crops and water for forestry, animal husbandry and freshwater aquaculture. The crop water requirement is the total amount of water consumed by transpiration by the plants and soil evaporation between the plants during the whole growth period and is an important factor that must be considered by irrigation decisionmaking and water resource planning [5]. Compared with that of crops, water consumption by forestry, animal husbandry and aquaculture industries is less affected by climate and is difficult to quantify.…”

Section: Introductionmentioning

confidence: 99%

Climate Change Impacts on Agricultural and Industrial Water Demands in the Beijing–Tianjin–Hebei Region Using Statistical Downscaling Model (SDSM)

Zhou,

Zhong,

Chen

et al. 2023

Water

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As a politically and culturally important city cluster, the Beijing–Tianjin–Hebei (BTH) region is the most prominent area in China where the imbalance between the supply and demand of water resources restricts the sustainable and healthy development of the regional social economy. In the context of global warming, research into water demand prediction that takes climate change into consideration would be more in line with the strategic goal of the low-carbon sustainable development of future cities. At the same time, the prediction of agricultural water demands against a background of climate change is urgently needed, while industrial water consumption is weakly correlated with climate change, an investigation of the statistical relationship between the two is needed. Thus, in this paper, future climate data from the BTH region under the scenarios RCP2.6, RCP4.5 and RCP8.5 were generated using a statistical downscaling model, and then coupled with agricultural and industrial water demand prediction models to simulate and analyze the impact of climate change on the agricultural and industrial water demands, respectively. The results show that during the forecast period (2020–2035), the reference crop evapotranspiration (ET0) growth rates in the Beijing, Tianjin and Hebei areas under the RCP2.6 scenario are 1.438 mm·a−1, 1.393 mm·a−1 and 2.059 mm·a−1, respectively. Under the RCP4.5 scenario, they are 2.252 mm·a−1, 2.310 mm·a−1 and 2.827 mm·a−1, respectively. Under the RCP8.5 scenario, they are 3.123 mm·a−1, 2.310 mm·a−1 and 2.141 mm·a−1, respectively. Furthermore, under each climate scenario, the increase in evapotranspiration in the Hebei area is the largest, followed by that in the Tianjin area, and that in the Beijing area is the smallest. For water consumption per CNY 10,000 of industrial added value during the forecast period, under the three different climate scenarios, a downward trend is seen in the Beijing area, with rates of 0.158, 0.153 and 0.110, respectively, but in the Tianjin area, there is an upward trend, with an upward tendency in rates of 0.170, 0.087 and 0.071, and an upward trend in the Hebei area, with an upward tendency in rates of 0.254, 0.071 and 0.036, respectively. This study will help the BTH region to rationally allocate agricultural and industrial water against the background of future climate change, and strengthen the coordination and cooperation between the different regions to promote the healthy and sustainable development of the cities.

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Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data

Zhao,

Qing,

et al. 2024

Int J Biometeorol

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Prediction of daily reference crop evapotranspiration in different Chinese climate zones: Combined application of key meteorological factors and Elman algorithm

Cited by 16 publications

References 27 publications

Improved GWO and its application in parameter optimization of Elman neural network

Improved GWO and its application in parameter optimization of Elman neural network

Climate Change Impacts on Agricultural and Industrial Water Demands in the Beijing–Tianjin–Hebei Region Using Statistical Downscaling Model (SDSM)

Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data

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