Optimal Irrigation with Perfect Weekly Forecasts versus Imperfect Seasonal Forecasts

Jamal, Alaa; Linker, Raphael; Housh, Mashor

doi:10.1061/(asce)wr.1943-5452.0001066

Cited by 19 publications

(9 citation statements)

References 21 publications

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“…They even did not consider weather forecast, but instead used the simple indirect method of [14]. Considering weather forecasts, the genetic algorithm was utilized to solve the couple simulation optimization: Deterministic and stochastic, to determine optimal irrigation depth and maximize seasonal net income [15,16].…”

Section: Introductionmentioning

confidence: 99%

Determining Irrigation Depths for Soybean Using a Simulation Model of Water Flow and Plant Growth and Weather Forecasts

2020

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A new scheme to determine irrigation depths using a two-point of predicted cumulative transpiration over irrigation interval is presented. Rather than maximizing water use efficiency, this scheme aims to maximize net income. The volumetric water price is considered to give farmers an incentive to save irrigation water. A field experiment for soybeans was carried out in the Arid Land Research Center, Tottori University, Japan in 2019. The total irrigation amount yield and net income by the proposed scheme were compared to those by a tensiometer-operated automated irrigation. The scheme could save irrigation water by 16% with a yield increment of 20%; resulting in a 22% increase in net income compared to the automated irrigation. The model simulated the volumetric water content in the effective root zone of the plant in fair agreement. These results indicate the effectiveness of the proposed scheme that may replace an automated irrigation system even considering uncertainty in weather forecast to determine irrigation depth and secure investment costs.

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Section: Introductionmentioning

confidence: 99%

Determining Irrigation Depths for Soybean Using a Simulation Model of Water Flow and Plant Growth and Weather Forecasts

2020

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“…Report generation only includes a module of report statistics, which requires the corresponding data calculation results to be produced according to the selected elements, and then displayed in the form of reports. The elements it needs to select include: region, time period, time, station type and forecast model [17][18].…”

Section: Figure 1 Overall Structure Of the Meteorological Data Analys...mentioning

confidence: 99%

Optimal Analysis of Weather Forecast Supporting Convolutional Neural Networks

2020

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In recent years, with the acceleration of social informatization, people's requirements for weather forecasting have gradually increased. Severe convective weather has attracted the attention of the meteorological department because of its characteristics of strong suddenness and great destructive power. As a forecasting method to prevent severe convective weather, short-term forecasting has important research significance. The purpose of this paper is to study the optimization analysis of meteorological forecasts supported by convolutional neural networks. In this paper, the deep learning method is used to conduct an applied research on the precipitation of short-term and imminent forecasting. Precipitation short-term forecasting is essentially the prediction of future radar echoes from a series of radar echo sequences, which can be regarded as a spatiotemporal sequence prediction problem. Based on the research and summary of commonly used neural networks, this paper refers to ConvLSTM (ConvolutionalLSTM) Structure A ConvGRU model (ConvolutionalGRU) combining Convolution Neural Network (CNN) and GRU (Gated Recurrent Unit) is proposed. Since the structure of GRU is simpler than that of LSTM, the effect is not much different. This model is compared to The ConvLSTM structure has faster training speed and smaller memory requirements. Another work of this paper is to improve the convolutional layer based on VGGNet (VisualGeometryGroupNet), using multiple small convolution kernels to stack instead of large convolution kernels, reducing the number of parameters and improving the feature extraction ability of the network. This model gives full play to the advantages of convolutional neural network and GRU, that is, the spatial feature extraction ability of convolutional structure and the memory ability of GRU to deal with time series problems. Finally, the prediction effects of the model and the optical flow method are compared through experiments to verify the applicability of the model in the short-term precipitation forecasting problem.

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“…Optimization of irrigation depths that maximize I n using WF and numerical simulation has been studied by several researchers. Some of them optimized irrigation depths corresponding to the maximal seasonal net income using a combination between genetic algorithms and the SWAP model [22][23][24]. However, they used simplified water-yield relationships in their calculations.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of a New Scheme for Determination of Irrigation Depths in the Egyptian Nile Delta

Baki

Fujimaki

2021

Water

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Innovative irrigation techniques should be implemented to improve irrigation management in dryland countries. In this regard, a new scheme, that uses three sets of irrigation depth and numerically simulated cumulative transpiration, was evaluated in the Egyptian Nile delta in 2020. Presuming that water is volumetrically priced, the proposed scheme can maximize net incomes at optimum irrigation depths considering quantitative weather forecasts. A field experiment was carried out with a randomized complete block design using a major crop, maize, to assess the feasibility of the proposed scheme in comparison to a sensor-based irrigation method under conditions of dry climate and clay loamy soil. The proposed scheme could increase the gross net income of farmers and conserve irrigation by 21% and 35%, respectively, compared to a sensor-based irrigation method, although the yield and its components were almost the same with no significant statistical differences. The model could accurately simulate soil water content in the topsoil layers with a RMSE of 0.02 cm3 cm−3. The proposed scheme could be a useful tool to spare the costs of expensive soil monitoring sensors while saving water and improving net income.

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Optimal Irrigation with Perfect Weekly Forecasts versus Imperfect Seasonal Forecasts

Cited by 19 publications

References 21 publications

Determining Irrigation Depths for Soybean Using a Simulation Model of Water Flow and Plant Growth and Weather Forecasts

Determining Irrigation Depths for Soybean Using a Simulation Model of Water Flow and Plant Growth and Weather Forecasts

Optimal Analysis of Weather Forecast Supporting Convolutional Neural Networks

An Evaluation of a New Scheme for Determination of Irrigation Depths in the Egyptian Nile Delta

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