Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques
Journal Title: Water Harvesting Research - Year 2024, Vol 7, Issue 2
Abstract
In this paper, the evaluation of the performance of five flood prediction models in the Simineh-Rood River, Lake Urmia basin, Iran, is discussed in detail. To this purpose, the performance of Transfer Function, Saint-Venant equations, Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Support Vector Machine models are evaluated for 2018 and 2019 flood data. Specifically, the models are rated according to their accuracy, computational efficiency, and robustness under different flow regimes and at various forecast times. This now leads to a maximum Nash-Sutcliffe Efficiency of 0.91 for the Saint-Venant equations during the 2019 flood event, followed by ANN with 0.89, ANFIS with 0.87, SVM with 0.85, and lastly, Transfer Function with 0.78. The same is the case for peak flow discharge, which was best predicted by the Saint-Venant model to be 193.80 m³/s while the observed value was 200.83 m³/s. This model maintained its consistency with respect to low, medium, and high flows, where the values of NSE were 0.89, 0.92, and 0.91, respectively. However, compared to the other models, which took 0.5â8 s, it had a much larger computational time, 120 s for a 72-h simulation. The sensitivity analysis returned variable model responses to the quality of the input data; an input variation of 20% reduced the NSE of the Saint-Venant model to 0.73 and that of the Transfer Function to 0.44. This study provides quantitative insight into the choice of flood prediction methods in a semi-arid region, with respect to required accuracy, computational resources, and forecast lead-time.
Authors and Affiliations
Jafar Chabokpour,
Keywords
Related Articles
- EP ID EP759871
- DOI -
- Views 31
- Downloads 0
Investigating the Variations in Water Requirement for Main Plants in the Cultivation Pattern (Case Study: Kashmar Plain of Khorasan Razavi)
The agricultural sector is crucial to Iran's economy, especially in ensuring food security. Climate changes, intense competition for water resources among various sectors, and the declining share of renewable resources i...
Evaluation and Comparison of Precipitation Datasets by Reanalysis and Satellite Models in Different Parts of Iran
Rainfall is a crucial component of the hydrological cycle and plays a key role in water resource planning. Recent research has investigated the use of gridded data as a supplement to and replacement for traditional rain...
Comparative Analysis of Machine Learning Algorithms for Forecasting Effluent Chemical Oxygen Demand in Wastewater Treatment Plants
Accurate prediction of wastewater effluent parameters is crucial for evaluating the performance of wastewater treatment plants, as it significantly contributes to reducing time, energy, and costs. This study employed thr...
Assessing Water Governance Gaps with a Four-Layer Governance Model and OECD Principles
In this study, water governance in four layers including the contextual layer, institutional layer, relational layer, and performance layer was evaluated using the 12 principles of the Organization of Economic Cooperatio...
Advancing Sustainable Agriculture: Renewable Energy Integration and Policy Implications for Irrigation in Nigeria – A Systematic Review
This research review explores the application of renewable energy sources, such as solar, wind, and biomass, in irrigation practices throughout Nigeria. Following the PRISMA (Preferred Reporting Items for Systematic Revi...