Paper Citation Record · LEDGER

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector

As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.10208.

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pith.paper-citation-record.v1
2607.10208 v1

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Reference resolution

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External citation measurements

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Outbound references

Observation 5dc49447-af61-40ff-9a83-eb65ab83efd4 · outbound

This paper cites an unresolved cited work.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Unresolved cited work

Reference 1

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Observation d2cbffb5-ddc2-4002-859b-0d2e78566dfd · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 2

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Observation 995ba79f-d10d-4e29-9e77-f7ae43b43059 · outbound

This paper cites Exploring machine learning, deep learning, and explainable ai methods for seasonal precipitation prediction in south america,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Exploring machine learning, deep learning, and explainable ai methods for seasonal precipitation prediction in south america,

Reference 3

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This paper cites Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments,

Reference 4

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Observation a65a6a17-1bd6-4892-9f5c-0fa05671fbad · outbound

This paper cites Comparison of machine learning algorithms,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Comparison of machine learning algorithms,

Reference 5

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This paper cites Learning to for- get: Continual prediction with lstm,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Learning to for- get: Continual prediction with lstm,

Reference 6

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Observation 98447670-ae2a-46c5-b8c5-9e4bdc22f570 · outbound

This paper cites ERA5 hourly data on single levels from 1940 to present,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector ERA5 hourly data on single levels from 1940 to present,

Reference 7

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Observation e2cde1ff-5f74-458d-ba11-ac9a5a34c5d6 · outbound

This paper cites The era5 global reanalysis,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector The era5 global reanalysis,

Reference 8

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This paper cites Long short-term mem- ory,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Long short-term mem- ory,

Reference 9

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Observation f83ac2cc-f068-4990-b469-139755a57cea · outbound

This paper cites State-of-the-art in 1d convolu- tional neural networks: A survey,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector State-of-the-art in 1d convolu- tional neural networks: A survey,

Reference 10

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Observation 7cdfd0b2-9271-44f0-9c0e-7076864987e5 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 11

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Observation cbc56eda-a18d-42dc-95fb-118cb61aeb26 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Adam: A Method for Stochastic Optimization

Reference 12

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Observation b172d1da-74ef-44fe-807f-286cefa20b25 · outbound

This paper cites Distributed systems and algorithms for measurement collection, decision making, and visualiza- tion of georeferenced information with applications in viticulture,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Distributed systems and algorithms for measurement collection, decision making, and visualiza- tion of georeferenced information with applications in viticulture,

Reference 13

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This paper cites Graphcast: Learning skillful medium-range global weather forecasting,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Graphcast: Learning skillful medium-range global weather forecasting,

Reference 14

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This paper cites GraphCast: Learning skillful medium-range global weather forecasting.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector GraphCast: Learning skillful medium-range global weather forecasting

Reference 15

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This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 16

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This paper cites Scikit-learn: Ma- chine learning in Python,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Scikit-learn: Ma- chine learning in Python,

Reference 17

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This paper cites Daily prediction and multi-step forward forecasting of reference evapotranspiration using LSTM and Bi-LSTM models,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Daily prediction and multi-step forward forecasting of reference evapotranspiration using LSTM and Bi-LSTM models,

Reference 18

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Observation 48f31367-6ca3-4b31-b342-08f25eae7fc7 · outbound

This paper cites A deep learning based framework for enhanced reference evapotranspiration estimation: Evaluating accuracy and forecasting strategies,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector A deep learning based framework for enhanced reference evapotranspiration estimation: Evaluating accuracy and forecasting strategies,

Reference 19

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This paper cites Dropout: A simple way to prevent neural networks from overfitting,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Dropout: A simple way to prevent neural networks from overfitting,

Reference 20

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This paper cites Water quality identification: Integrating iot sensors and deep learning for near-real-time water quality assessment,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Water quality identification: Integrating iot sensors and deep learning for near-real-time water quality assessment,

Reference 21

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This paper cites Development of an in vivo sensor to monitor the effects of vapour pressure deficit (VPD) changes to improve water productivity in agriculture,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Development of an in vivo sensor to monitor the effects of vapour pressure deficit (VPD) changes to improve water productivity in agriculture,

Reference 22

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Observation 1c80716a-01c7-4aaf-b272-b8ef953fdfa8 · outbound

This paper cites Open-Meteo.com Weather API,.

Exploratory Analysis of Deep Learning Models for Forecasting Meteorological Parameters in the Agricultural Sector Open-Meteo.com Weather API,

Reference 23

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