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In Situ Soil Moisture Sensors in Undisturbed Soils
Published on: November 18, 2022
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Drought prediction using artificial intelligence models based on climate data and soil moisture
Mhamd Saifaldeen Oyounalsoud1, Abdullah Gokhan Yilmaz2, Mohamed Abdallah3,4
1Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates.
Scientific Reports
|August 24, 2024
Summary
This study developed new artificial intelligence (AI) drought indices, outperforming conventional methods for more accurate drought forecasting and monitoring. AI offers a reliable approach for better drought assessment and mitigation strategies.
Area of Science:
- Environmental Science
- Climate Science
- Data Science
Background:
- Drought poses significant global economic and social risks, necessitating effective monitoring and management tools.
- Existing meteorological drought indices face limitations due to the complexity of drought phenomena and varying hydroclimatic conditions, preventing universal application.
- The absence of a universally applicable drought index highlights the need for advanced methodologies in drought assessment.
Purpose of the Study:
- To develop and evaluate novel artificial intelligence (AI)-based meteorological drought indices for improved drought description and forecasting.
- To compare the performance of AI-derived indices against conventional drought indices using historical drought indicator data.
- To assess the reliability of AI models, including decision tree, generalized linear model, support vector machine, artificial neural network, deep learning, and random forest, in drought prediction.
Main Methods:
- Development of AI-based drought indices using decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest models.
- Training and validation of AI models using diverse climatic datasets from Alice Springs, Australia.
- Comparative analysis of AI-derived indices against nine conventional drought indices, correlating them with historical records of runoff and various soil moisture levels (deep, lower, root, upper).
Main Results:
- The rainfall anomaly drought index showed the highest correlation (0.718) with upper soil moisture among conventional indices.
- The DT-based AI index exhibited the strongest correlation (0.97) with the rainfall anomaly index, while the GLM-based index showed the lowest (0.57) with the Palmer drought severity index.
- The GLM-based index demonstrated superior performance with a correlation coefficient of 0.78 for upper soil moisture, indicating AI-based indices generally outperform conventional ones.
Conclusions:
- Developed AI-based drought indices provide more accurate drought forecasting and monitoring capabilities compared to traditional methods.
- Artificial intelligence presents a promising and reliable approach for enhancing drought assessment and mitigation strategies.
- The study underscores the potential of AI in addressing the complexities of meteorological drought prediction.
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