Artificial intelligence-inspired comprehensive framework for desertification prediction

Shtwai Alsubai , Mogeeb A.A. Mosleh , Suheer A. Al-Hadhrami , Munish Bhatia
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Abstract

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals.
Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification.
This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulating environmental sustainability policies.
基于人工智能的沙漠化预测综合框架
荒漠化严重影响土地退化、农业效率、经济增长和健康,尤其是在海湾国家。气候变化加剧了荒漠化,使该地区的发展问题更加困难。本研究提出了一个利用物联网(IoT)进行持续监测、数据收集和分析以评估荒漠化模式的增强框架。该框架利用贝叶斯信念网络(BBN)对物联网数据进行分类,而边缘计算平台上的低延迟处理方法能够有效检测荒漠化趋势。随后,使用遗传算法优化的人工神经网络(ANN)对分类数据进行分析,以进行预测决策。利用云计算基础设施,ANN-GA模型检查复杂的数据连接,以预测荒漠化风险要素。采用自回归综合移动平均(ARIMA)模型对不同时间区间的沙漠化进行预测。实验模拟证明了所建议框架的有效性,在基本指标上获得了增强的性能:时间延迟(103.68 s),分类效率-灵敏度(96.44%),精度(95.56%),特异性(96.97%),F-Measure(96.69%) -预测效率-准确度(97.76%)和均方根误差(RMSE)(1.95%) -以及可靠性(93.73%)和稳定性(75%)。分类有效性和预测性能的结果强调了框架检测高风险区域和预测荒漠化严重程度的能力。这一创新方法提高了对荒漠化过程的理解,鼓励可持续土地管理做法,减少荒漠化的社会经济影响,并加强处于危险中的生态系统。这项研究的结果对于加强区域防治荒漠化、确保粮食安全和制定环境可持续性政策具有相当重要的意义。
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CiteScore
13.80
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