An improved deep learning procedure for statistical downscaling of climate data

Kheir, Ahmed M.S.; Elnashar, Abdelrazek; Mosad, Alaa; Govind, Ajit

doi:10.1016/j.heliyon.2023.e18200

Cited by 11 publications

(6 citation statements)

References 63 publications

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“…These models can handle high-dimensional predictor variable spaces, automatically selecting variables and geographical regions that influence each site during the downscaling process. This is crucial because modern statistical downscaling methods, such as mature Generalized Linear Models, struggle to handle such high dimensionality without overfitting, often requiring some form of manually guided feature selection (resulting in the loss of relevant information) [28]. This study used high-dimensional input grids and various predictor variables to test the CNN model.…”

Section: Data Preprocessing and Statistical Downscaling Based On The ...mentioning

confidence: 99%

Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

Zhou,

Luo,

Yuan

2024

Atmosphere

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Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy’s future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s−1. The overall wind speed distribution range is significant, meeting the requirements for wind farm development.

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Section: Data Preprocessing and Statistical Downscaling Based On The ...mentioning

confidence: 99%

Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

Zhou,

Luo,

Yuan

2024

Atmosphere

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“…The ANN algorithm was built in Python and executed in Google Colab as a cloud computing environment, and the source code is hosted on an open‐source project on GitHub (https://github.com/DrAhmedKheir/tANN-.git). We selected ANN rather than other ML algorithms because deep learning has different hidden layers and can learn and model nonlinear and complex relationships, which is critical because many of the relationships between inputs and outputs in real life are nonlinear and complex (Kheir, Ammar, Attia et al., 2022; Kheir et al., 2023). ML approaches have some advantages over crop models, including the capacity to involve additional input variables that crop models cannot.…”

Section: Apsimx_r_ml Approachmentioning

confidence: 99%

Integrating APSIM model with machine learning to predict wheat yield spatial distribution

Kheir,

Mkuhlani,

Mugo

et al. 2023

Agronomy Journal

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Traditional simulation models are often point based, thus more research is needed to emphasize the spatial simulation, providing decision makers with fast recommendations. Combining machine learning algorithms with spatial process‐based models could be considered an appropriate solution. We created a spatial model in R (APSIMx_R) to generate fine resolution data from coarse resolution data, which is typically available at the regional level. The APSIM crop model outputs were then deployed to train and test the Artificial Neural Network (ANN), creating a hybrid modelling approach for robust spatial simulations. The APSIMx_R package facilitates preparing the required model inputs, executes the prediction, processes, and analyzes the APSIM crop model outputs. This note demonstrates the use of a new approach for creating reproducible crop modelling workflows with the spatial APSIM next generation model and machine learning algorithms. The tool was deployed for spatial and temporal simulation of potential wheat yield under different nitrogen rates, and various wheat cultivars. The spatial APSIMx_R was validated by comparing the simulated yield at 100 kg Nha−1 to the analogues' actual yield at the same grid points, which showed good agreement (d = 0.89), between spatially predicted and actual yield. The hybrid approach increased such precision, resulting in higher agreement (d = 0.95) with actual yield. When the interaction between cultivars and nitrogen levels was considered, it was found that the novel cultivar Sakha95 is nitrogen voracious, exhibiting a larger drop in yield (65%) under minimal nitrogen treatment (0 kg N ha−1) relative to the potential yield.This article is protected by copyright. All rights reserved

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“…To this end, this research considers it is important to bear these factors in mind when discussing and assessing the sustainability of the sector. The space occupied by crops represents 37% of the arable land surface, and the use of water for these corresponds to almost 2/3 of the total area of arable land [ 4 , 5 ]. One of the effects on the environment that of pollution by nitrates, phosphates, and pesticides, which act as a source of production of greenhouse gases, methane, and nitrous oxide which affect the quality of both air and water [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Characterisation and causal model of the holistic dynamics of the integral sustainability of the agri-food system

Leon-Romero,

Zamora-Polo,

Luque-Sendra

et al. 2024

PLoS ONE

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The transformation of the food and industrial agricultural production system into adaptative and sustainable systems capable of being productive within social, environmental, and economic limits is a crucial factor in reducing the risk to food security and to economic growth. However, the analysis structure of the effect of these variables in sustainable environments remains unknown, whereby the technology and processes are considered as variables of the equivalent critical level as those already described. The purpose of this study is to design a model that enables the characterisation of the agri-food sector based on the determination of sustainable variables from a sustainable and integral systemic approach. Tools, such as the viable system model, are employed to analyse the dynamics and generate the balanced scorecard, to which the items of learning and continuous improvement are added. Lastly, the impact of the principles of sustainability versus the variation of sustainability in the agri-food system is revealed, which is useful in determining the appropriate levels to guarantee a balance in the foundations of circularity. From a systemic approach, this model can be adopted by agronomists and scientists to design alternative strategies for the management of food sustainability.

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An improved deep learning procedure for statistical downscaling of climate data

Cited by 11 publications

References 63 publications

Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

Integrating APSIM model with machine learning to predict wheat yield spatial distribution

Characterisation and causal model of the holistic dynamics of the integral sustainability of the agri-food system

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