Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages

Abstract

Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.

Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions

“…Also, evaluating the agricultural drought status, which is done by famous indicators such as standardized precipitation-evapotranspiration index (SPEI) and Palmer drought severity index (PDSI), directly requires the monthly scale ET0 rate of the region. Data-driven models like stochastic and artificial intelligence methods are efficient approaches that have shown good performance in modeling and predicting hydrometeorological variables in recent years (Essam et al 6 ; Dehghanisanij et al 7 ; Elbeltagi et al 8 ; Azad et al 9 ; Zhang et al 10 ; Zarei et al 11 ; Graf and Aghelpour 12 ; Chen et al 13 ). In ET0 cases, Karbasi 14 have used AIs for ET0 forecasting in 1, 2, 3, 7, 10, 14, 18, 24, and 30 days lead times.…”

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

“…Multi-objective and scenario analysis are typical applications of GeoAI techniques in IWRM to find solutions for conflicting objectives, forecast the impact of management strategies, and optimize hydrological system operation [157,158]. We found widespread applications of GeoAI in reservoir and water distribution optimization using ANN [159,160], assembled and deep learning algorithms, and genetic programming [161,162]. Another application is found in building a smart irrigation decision support system [147].…”

Hierarchical Temporal Scale Data-driven Reservoir Operation Modeling

“…As hydrologic models and observations continue to improve and provide better prediction, the ultimate question is how hydrologic prediction (and what types of prediction) can be effectively utilized to improve the operation of reservoirs. Although we find very limited case where hydrologic forecast is used in operation in the 300 reservoirs, there are efforts to explore the reservoir operation using streamflow prediction (Delaney et al, 2020;Zarei et al, 2021).…”