Forecasting Rainfall with Recurrent Neural Network for irrigation equipment

Zhou, Junyu; Zhang, Yubin; Si, Tian; Lai, Shangding

doi:10.1088/1755-1315/510/4/042040

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…AI systems with sensors are used for collecting weather data and predict its future petters for decision making. Adaptive-neuro Fuzzy Inference System based rain prediction model was developed in [14], [15] and the study in [16], [17] Recurrent Neuro Network rainfall prediction model was proposed for irrigation purpose. These AI based approaches collected weather data including temperature, humidity, rainfall, wind speed and solar radiation and processes them for the purposes of predicting the probable amount of rainfall.…”

Section: Related Workmentioning

confidence: 99%

AI Based Real-Time Weather Condition Prediction with Optimized Agricultural Resources

Pierre,

Ishimwe Viviane,

Lambert

et al. 2023

EJT

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Purpose: Unpredictable and rapid change in weather patterns has great impact on agricultural activities, especially for precision agriculture that results in worsened water resources availability, decreased soil fertility, use of pesticide as well as decreased yield productivity. In attempt to alleviate these challenges, this study aims at developing a real-time weather and farm field data driven Artificial Intelligence (AI) and Internet of Things (IoT) system that analyze, manage and schedule irrigation and fertigation as well as enabling farmers to interact with their farms via Smart phone or PCs to optimize energy and water resources. Methodology: The system employs weather condition monitoring sensors such as atmospheric pressure, air temperature, air humidity and wind speed for collecting real-time farm field data and uses Fuzzy Inference System (FIS) to predict rainfall rate at farm area for 24 hours period. The system also gathers field data such as soil moisture content and soil nutrient content and uses the Machine Learning (ML) algorithms to predict the time for irrigation and fertigation. By combining weather and farm field data, the system schedules the irrigation and fertigation activity. In addition, the mobile application is developed for the farmers to interact, control and monitor the farming activities and the data is presented to the farmers in both graphical and numerical formats. Findings: The system prototype deployed and tested in the two hectors Maize farm proved that 55% of water, 51% of energy and 20% of fertilizer were saved as well as increases in 20% of Maize yield production compared to previous season. Recommendations: Since the current irrigation and fertigation practices are based on predetermined time of the day and threshold values for automatic irrigation, this solution introduced the new concept of real-time and short-term weather forecasting that enables farmers to balance the irrigation period and weather pattern for water and fertilizer resources optimization.

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Section: Related Workmentioning

confidence: 99%

AI Based Real-Time Weather Condition Prediction with Optimized Agricultural Resources

Pierre,

Ishimwe Viviane,

Lambert

et al. 2023

EJT

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“…One of the main advantages of DL is that in certain circumstances, the model performs the feature extraction process. DL models have significantly enhanced the stateof-the-art in a variety of sectors and industries, including agriculture [149][150][151][152][153][154], where they are often used for image and sound processing. DL models are essentially ANNs with many hidden layers between the input and output layers, with recurrent neural network (RNN), long short-term memory (LSTM), and convolutional neural network (CNN) being examples of supervised and unsupervised learning techniques for irrigation decision optimization.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

“…The simulation result showed a good prediction performance of R 2 of 0.94 and RMSE of less than 1.2 mm, with the possibility of being used as a decision support system for irrigation scheduling [151]. Similar work was implemented using RNN with LSTM, a feedforward neural network, wavelet neural network, and ARIMA for forecasting rainfall, with an RNN-based LSTM having a better forecasting performance when compared with other models [152]. Another work on the use of the RNN model for optimal water allocation of irrigation during droughts, through forecasting annual irrigation inflow based on climate and hydrological data and optimization, scheduled water among the irrigation units by considering the crop coefficient and water stress at different growth stages [153].…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

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“…Precipitation is the main forecast element of Numerical Weather Prediction [ 1 ] (NWP), which has an important impact on people’s daily life [ 2 , 3 ] and disaster prevention and mitigation [ 4 ]. After years of development, the current short-term and medium-term NWP models have become more and more accurate.…”

Section: Introductionmentioning

confidence: 99%

RN-Net: A Deep Learning Approach to 0–2 Hour Rainfall Nowcasting Based on Radar and Automatic Weather Station Data

Zhang

Wang

Guan

et al. 2021

Sensors

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Precipitation has an important impact on people’s daily life and disaster prevention and mitigation. However, it is difficult to provide more accurate results for rainfall nowcasting due to spin-up problems in numerical weather prediction models. Furthermore, existing rainfall nowcasting methods based on machine learning and deep learning cannot provide large-area rainfall nowcasting with high spatiotemporal resolution. This paper proposes a dual-input dual-encoder recurrent neural network, namely Rainfall Nowcasting Network (RN-Net), to solve this problem. It takes the past grid rainfall data interpolated by automatic weather stations and doppler radar mosaic data as input data, and then forecasts the grid rainfall data for the next 2 h. We conduct experiments on the Southeastern China dataset. With a threshold of 0.25 mm, the RN-Net’s rainfall nowcasting threat scores have reached 0.523, 0.503, and 0.435 within 0.5 h, 1 h, and 2 h. Compared with the Weather Research and Forecasting model rainfall nowcasting, the threat scores have been increased by nearly four times, three times, and three times, respectively.

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Forecasting Rainfall with Recurrent Neural Network for irrigation equipment

Cited by 7 publications

References 4 publications

AI Based Real-Time Weather Condition Prediction with Optimized Agricultural Resources

AI Based Real-Time Weather Condition Prediction with Optimized Agricultural Resources

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

RN-Net: A Deep Learning Approach to 0–2 Hour Rainfall Nowcasting Based on Radar and Automatic Weather Station Data

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