Accuracy Enhancement AI-Based River Water-Level Prediction Model | #Sciencefather #Researcherawards

                         


Introduction

Global warming has evolved into one of the most pressing climate crises of the 21st century, extending beyond a simple rise in global temperatures. Its far-reaching impacts include increasing occurrences of extreme weather phenomena, especially in Asia. In regions such as Korea and the ASEAN member states, the escalation in the frequency and intensity of extreme rainfall events has significantly amplified flood-related disasters. These large-scale hydrological events highlight the necessity for advanced flood-forecasting models that can provide early warnings and support mitigation strategies. With artificial intelligence (AI) emerging as a promising alternative to traditional physics-based methods, researchers are focusing on developing AI-driven systems that can predict floods with higher accuracy and speed using limited datasets.

Impact of Climate Change on Hydrological Systems

The accelerating pace of climate change has had profound effects on hydrological cycles across Asia. Rising global temperatures have altered precipitation patterns, leading to irregular rainfall and an increase in flash flood incidents. Korea and the ASEAN countries are particularly vulnerable due to their diverse climatic and geographical conditions. This shift in hydrological dynamics demands new analytical tools capable of understanding and forecasting water-level variations with precision. Conventional models often fail to capture nonlinear dependencies in climatic data, necessitating innovative approaches that integrate AI and data science into hydrological research.

Advancements in AI-Based Flood Forecasting

Artificial intelligence has revolutionized environmental prediction models by enabling faster and more efficient data processing. In this study, a Long Short-Term Memory (LSTM)-based model—a form of recurrent neural network—was used to forecast river water levels in real time. Unlike traditional models, the LSTM approach can learn temporal dependencies and nonlinear relationships inherent in hydrological data. The model’s capacity to handle sequential data makes it particularly suitable for dynamic environments where water levels fluctuate rapidly. This AI-driven strategy represents a significant step forward in building accurate, adaptive, and data-efficient flood-forecasting frameworks.

Methodological Enhancements Using Differencing and Cross-Validation

To enhance model performance, the research implemented water-level differencing, which transformed nonstationary datasets into stationary ones, improving prediction stability. Additionally, a repeated k-fold cross-validation method was used to strengthen the model’s generalization ability and reduce overfitting. These combined techniques ensure that the model remains robust even when limited observational data are available. Such methodological refinements contribute to more reliable real-time forecasting and reinforce the practicality of AI applications in flood management systems.

Performance Evaluation and Results

The LSTM-based flood prediction model was tested across observation stations in the Philippines, Indonesia, and the Republic of Korea. Performance evaluation metrics—including Root Mean Square Error (RMSE), Coefficient of Determination (R²), Nash–Sutcliffe Efficiency (NSE), and Kling–Gupta Efficiency (KGE)—indicated superior results for the differenced model compared to the baseline. The model achieved an RMSE of 0.13 m, R² of 0.866, NSE of 0.844, and KGE of 0.893, demonstrating an approximate 17% improvement in accuracy. These findings highlight the potential of LSTM-based AI frameworks in achieving precise, data-driven flood forecasts for diverse hydrological regions.

Future Prospects and Regional Implementation

The successful deployment of the proposed AI model opens new avenues for implementing real-time flood prediction systems in data-scarce regions across Southeast Asia. Integrating such frameworks into regional early warning systems could significantly enhance disaster preparedness and reduce socio-economic losses. Future research aims to apply this approach to various hydrological contexts to validate and expand its applicability. As climate change intensifies, the fusion of AI technology with hydrological science will play a pivotal role in safeguarding vulnerable communities and advancing sustainable water resource management.


Global Particle Physics Excellence Awards


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#Sciencefather, #Reseacherawards, #GlobalWarming, #FloodForecasting, #ClimateChange, #ArtificialIntelligence, #LSTMModel, #HydrologyResearch, #MachineLearning, #DeepLearning, #ASEAN, #Korea, #RiverPrediction, #EnvironmentalScience, #DataDrivenModels, #DisasterManagement, #WaterLevelPrediction, #SustainableDevelopment, #AIinClimate, #HydrologicalModeling, #CrossValidation, #ResearchInnovation,

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