Financial Risk Prediction for Agricultural Enterprises Using Intelligent Modeling and Dynamic State Analysis Article Swipe
Hemal Thakker
,
Amit Kumar Shrivastav
,
Princy AS
,
Ansuman Samal
,
Fazil Hasan
,
Babitha Babitha
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.62486/agma2025179
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.62486/agma2025179
Agricultural enterprises have financial uncertainties due to market volatility, climate disruptions, and changes in policies; therefore, farming operations must use timely and accurate forecasts, as they are particularly vulnerable to external economic shocks and environmental variability. Standard forecasting methods usually cannot capture nonlinear dependencies and dynamic shifts in risk profiles; therefore, there is a need to consider intelligent, adaptive systems. Research proposes a novel financial risk prediction model using the Sooty Tern Optimization Algorithm Attention-Based Long Short-Term Memory (STOA-Att-LSTM). Financial risk data were collected, which included agricultural enterprise financial records, national weather databases, and commodity market indices. To ensure data integrity and modelling efficiency, two essential pre-processing techniques were employed. Handling missing values was performed using linear interpolation to reconstruct incomplete sequences, particularly in time-series financial and climatic data, to standardize variables, facilitating efficient model training and convergence. The STOA algorithm was used to optimize the hyper-parameters of the Att-LSTM model, enhancing its generalization and predictive accuracy. The attention mechanism enabled the model to dynamically focus on critical time-dependent features influencing financial risk. Dynamic state analysis further strengthened the framework by capturing temporal shifts in enterprise conditions. Model evaluation using Python-based implementation of error metrics and classification accuracy (0.9899) showed better results compared to traditional and baseline deep learning (DL) models. The proposed framework offers a robust, adaptive tool for proactive financial risk assessment in agricultural enterprises, supporting sustainable decision-making in uncertain environments.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.62486/agma2025179
- https://managment.ageditor.uy/index.php/managment/article/download/179/504
- OA Status
- diamond
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413005818
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4413005818Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.62486/agma2025179Digital Object Identifier
- Title
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Financial Risk Prediction for Agricultural Enterprises Using Intelligent Modeling and Dynamic State AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-01Full publication date if available
- Authors
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Hemal Thakker, Amit Kumar Shrivastav, Princy AS, Ansuman Samal, Fazil Hasan, Babitha BabithaList of authors in order
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https://doi.org/10.62486/agma2025179Publisher landing page
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https://managment.ageditor.uy/index.php/managment/article/download/179/504Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://managment.ageditor.uy/index.php/managment/article/download/179/504Direct OA link when available
- Concepts
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Agriculture, State (computer science), Finance, Business, Computer science, Geography, Algorithm, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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12Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.climate | 9 |
| abstract_inverted_index.dynamic | 45 |
| abstract_inverted_index.enabled | 160 |
| abstract_inverted_index.farming | 16 |
| abstract_inverted_index.further | 176 |
| abstract_inverted_index.methods | 38 |
| abstract_inverted_index.metrics | 194 |
| abstract_inverted_index.missing | 111 |
| abstract_inverted_index.models. | 210 |
| abstract_inverted_index.results | 201 |
| abstract_inverted_index.robust, | 216 |
| abstract_inverted_index.usually | 39 |
| abstract_inverted_index.weather | 91 |
| abstract_inverted_index.(0.9899) | 198 |
| abstract_inverted_index.Att-LSTM | 149 |
| abstract_inverted_index.Handling | 110 |
| abstract_inverted_index.Research | 60 |
| abstract_inverted_index.Standard | 36 |
| abstract_inverted_index.accuracy | 197 |
| abstract_inverted_index.accurate | 22 |
| abstract_inverted_index.adaptive | 58, 217 |
| abstract_inverted_index.analysis | 175 |
| abstract_inverted_index.baseline | 206 |
| abstract_inverted_index.climatic | 127 |
| abstract_inverted_index.compared | 202 |
| abstract_inverted_index.consider | 56 |
| abstract_inverted_index.critical | 167 |
| abstract_inverted_index.economic | 31 |
| abstract_inverted_index.external | 30 |
| abstract_inverted_index.features | 169 |
| abstract_inverted_index.included | 85 |
| abstract_inverted_index.indices. | 96 |
| abstract_inverted_index.learning | 208 |
| abstract_inverted_index.national | 90 |
| abstract_inverted_index.optimize | 144 |
| abstract_inverted_index.proposed | 212 |
| abstract_inverted_index.proposes | 61 |
| abstract_inverted_index.records, | 89 |
| abstract_inverted_index.systems. | 59 |
| abstract_inverted_index.temporal | 182 |
| abstract_inverted_index.training | 135 |
| abstract_inverted_index.Algorithm | 73 |
| abstract_inverted_index.Financial | 79 |
| abstract_inverted_index.accuracy. | 156 |
| abstract_inverted_index.algorithm | 140 |
| abstract_inverted_index.attention | 158 |
| abstract_inverted_index.capturing | 181 |
| abstract_inverted_index.commodity | 94 |
| abstract_inverted_index.efficient | 133 |
| abstract_inverted_index.employed. | 109 |
| abstract_inverted_index.enhancing | 151 |
| abstract_inverted_index.essential | 105 |
| abstract_inverted_index.financial | 3, 64, 88, 125, 171, 221 |
| abstract_inverted_index.framework | 179, 213 |
| abstract_inverted_index.integrity | 100 |
| abstract_inverted_index.mechanism | 159 |
| abstract_inverted_index.modelling | 102 |
| abstract_inverted_index.nonlinear | 42 |
| abstract_inverted_index.performed | 114 |
| abstract_inverted_index.policies; | 14 |
| abstract_inverted_index.proactive | 220 |
| abstract_inverted_index.profiles; | 49 |
| abstract_inverted_index.uncertain | 231 |
| abstract_inverted_index.Short-Term | 76 |
| abstract_inverted_index.assessment | 223 |
| abstract_inverted_index.collected, | 83 |
| abstract_inverted_index.databases, | 92 |
| abstract_inverted_index.enterprise | 87, 185 |
| abstract_inverted_index.evaluation | 188 |
| abstract_inverted_index.forecasts, | 23 |
| abstract_inverted_index.incomplete | 120 |
| abstract_inverted_index.operations | 17 |
| abstract_inverted_index.prediction | 66 |
| abstract_inverted_index.predictive | 155 |
| abstract_inverted_index.sequences, | 121 |
| abstract_inverted_index.supporting | 227 |
| abstract_inverted_index.techniques | 107 |
| abstract_inverted_index.therefore, | 15, 50 |
| abstract_inverted_index.variables, | 131 |
| abstract_inverted_index.vulnerable | 28 |
| abstract_inverted_index.conditions. | 186 |
| abstract_inverted_index.dynamically | 164 |
| abstract_inverted_index.efficiency, | 103 |
| abstract_inverted_index.enterprises | 1 |
| abstract_inverted_index.forecasting | 37 |
| abstract_inverted_index.influencing | 170 |
| abstract_inverted_index.reconstruct | 119 |
| abstract_inverted_index.standardize | 130 |
| abstract_inverted_index.sustainable | 228 |
| abstract_inverted_index.time-series | 124 |
| abstract_inverted_index.traditional | 204 |
| abstract_inverted_index.volatility, | 8 |
| abstract_inverted_index.Agricultural | 0 |
| abstract_inverted_index.Optimization | 72 |
| abstract_inverted_index.Python-based | 190 |
| abstract_inverted_index.agricultural | 86, 225 |
| abstract_inverted_index.convergence. | 137 |
| abstract_inverted_index.dependencies | 43 |
| abstract_inverted_index.disruptions, | 10 |
| abstract_inverted_index.enterprises, | 226 |
| abstract_inverted_index.facilitating | 132 |
| abstract_inverted_index.intelligent, | 57 |
| abstract_inverted_index.particularly | 27, 122 |
| abstract_inverted_index.strengthened | 177 |
| abstract_inverted_index.variability. | 35 |
| abstract_inverted_index.environmental | 34 |
| abstract_inverted_index.environments. | 232 |
| abstract_inverted_index.interpolation | 117 |
| abstract_inverted_index.uncertainties | 4 |
| abstract_inverted_index.classification | 196 |
| abstract_inverted_index.generalization | 153 |
| abstract_inverted_index.implementation | 191 |
| abstract_inverted_index.pre-processing | 106 |
| abstract_inverted_index.time-dependent | 168 |
| abstract_inverted_index.Attention-Based | 74 |
| abstract_inverted_index.decision-making | 229 |
| abstract_inverted_index.(STOA-Att-LSTM). | 78 |
| abstract_inverted_index.hyper-parameters | 146 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.39276957 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |