Reliable runoff prediction plays a significant role in reservoir scheduling, water resources management, and efficient utilization of water resources. To effectively enhance the prediction accuracy of monthly runoff series, a hybrid prediction model (TVF-EMD-SSA-ELM) combining time varying filtering (TVF) based empirical mode decomposition (EMD), salp swarm algorithm (SSA) and extreme learning machine (ELM) is proposed. Firstly, the monthly runoff series is decomposed into several sub-series using TVF-EMD. Secondly, SSA is used to optimize the input weights and hidden layer biases of the selected ELM model. Finally, the prediction results are generated by summing and reconstructing each sub-series based on the SSA optimized ELM model. This hybrid model is applied to the monthly runoff prediction of Manwan hydropower, Hongjiadu hydropower, and Yingluoxia hydrological station, and compared with back propagation (BP), ELM, SSA-ELM, PSO-ELM, GSA-ELM, TVF-EMD-ELM, EMD-SSA-ELM, extreme-point symmetric mode decomposition (ESMD)-SSA-ELM, wavelet decomposition (WD)-SSA-ELM and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-SSA-ELM models. The prediction performance of various models is reflected by four evaluation indicators (R, NSEC, NRMSE, MAPE). Results reveal that the prediction effect of the ELM model is better than that of BP, the optimization accuracy of SSA is better than those of particle swarm optimization (PSO) and gravitational search algorithm (GSA), and the prediction accuracy of the hybrid TVF-EMD and SSA is better than that of only TVF-EMD or SSA. TVF-EMD-SSA-ELM model has the highest prediction accuracy. When compared with the single ELM model, it’s NRMSE and MAPE at Manwan hydropower decrease by 84.4% and 72.38%, those of Hongjiadu hydropower decrease by 85.21% and 78.38%, and those of Yingluoxi hydrological station decrease by 68.42% and 39.51%, respectively. R and NSEC of the three sites are close to 1. Therefore, the proposed model provides a new method for the prediction of monthly runoff, and the results can provide a reference for the prediction of monthly runoff in the study area. 

An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical mode decomposition

The important foundation for water resource management and utilization is effective monthly runoff prediction. In this study, a new coupled model for predicting monthly runoff is proposed. In order to predict the decomposed subsequences separately using an ELMAN neural network optimized by the sparrow search algorithm (SSA), this model first decomposes the original runoff series using robust local mean decomposition (RLMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) predicts the decomposed subsequences separately using an ELMAN neural network optimized by the sparrow search algorithm (SSA), and reconstructs the results to get initial prediction results. To acquire the final prediction result, local error correction (LEC) is used to perform error correction on the initial prediction model. Five evaluation indicators are used to assess the performance of the suggested coupling model on monthly runoff data from three experimental stations in China. After having analyzed the error correction capability of LEC model, it is found that CEEMDAN-RLMD-SSA-ELMAN-LEC decreases root mean square error (RMSE) values of Manwan Hydropower, Jiayuguan Station, and Yingluoxia Station by 18.52%, 21.78%, and 38.80%, respectively, and increases Nash-Sutcliffe efficiency coefficient (NSEC) values by 2.11%, 4.49%, and 5.43% compared to CEEMDAN-RLMD-SSA-ELMAN without error correction. Therefore, by incorporating error correction, the proposed coupled model is a trustworthy and beneficial means for forecasting monthly runoff. 

A new hybrid model for monthly runoff prediction using ELMAN neural network based on decomposition-integration structure with local error correction method

Hydropower depends on the elevation head and water flow of a river. However, other factors must be considered, such as the risk associated with surface processes and environmental factors. The study aims to analyze a landscape’s dynamics and locate potential sites for small-scale hydropower systems (<10 MW) using a geographic information system, the curve number method, and the TopoToolbox with a digital elevation model and available spatial datasets. Across Bohol Island in the central Philippines, the study found 94 potential sites with hydraulic heads ranging from 20–62.4 m, river discharges between 0.02 to 9.71 m3/s, and a total hydropower capacity of 13.595 MW. The river profile analysis classified the sites to five levels of risk to geo-hazards, with three-fourths of the sites being at ‘high’ to ‘very high’ risk levels while more than 50% of the total power can be generated in ‘low’ risk areas. Land-use and population constraints reduced the sites to 25 and the hydropower capacity by 60%. Although limited to the table assessment phase of hydropower development, the study showed the potential of small-scale hydropower systems in the study area, their spatial distribution, and the risk associated with each site. The study results provided data-limited resource managers’ and energy planners’ insights in targeting potential locations and minimizing field investigation costs and time.

/pdf/incorporating-landscape-dynamics-in-small-scale-hydropower-1atfbv0y.pdf

Incorporating Landscape Dynamics in Small-Scale Hydropower Site Location Using a GIS and Spatial Analysis Tool: The Case of Bohol, Central Philippines

The recent energy shortfall in Pakistan has prompted the need for the development of hydropower projects to cope with the energy and monetary crisis. Hydropower in the northern areas is available yet has not been explored too much. Focusing on the sustainable development goal (SDG) “Ensure access to affordable, reliable, sustainable and modern energy”, thirteen proposed sites were selected from upstream to downstream of the Hunza River for analysis. The head on all the proposed sites was determined by taking the elevation difference between the proposed turbine and the intake at all sites. The discharge on all proposed ungauged sites was determined using ArcGIS for watershed delineation and the area ratio method along with Soil Conservation Service–Curve Number (SCS-CN) by using gauged data of Hunza River provided by Water and Power Development Authority (WAPDA) Pakistan at Daniyor bridge Gilgit, Shimshal and the Passo tributaries of Hunza River. The Location Search Algorithm (LSA) approach used a multi-criteria decision-making tool (MDM) to make a decision matrix considering the location and constraint criteria and then normalizing the decision matrix using benefit and cost criteria, the relative weights were assigned to all criteria using a rank sum weighted method and the sites were ranked on the basis of the final score. The results revealed that Hunza River has a significant hydropower potential and based on the final score in the LSA approach, proposed site 13, site 4 and site 9 were concluded as the most promising sites among proposed alternatives. The proposed methodology could be an encouraging step for decision makers for future hydropower development in Pakistan. 

Ascertainment of Hydropower Potential Sites Using Location Search Algorithm in Hunza River Basin, Pakistan

Renewable energy sources have begun to arouse interest worldwide. The UN Sustainable Development Agenda has defined access to affordable, reliable, sustainable and modern energy as one of its main goals. This paper contains a description of the research on the assessment of the validity of employing surveying methods to capture topographic data in order to select locations suitable for small hydropower plants. For the purpose of this study, a section of a natural watercourse with the surrounding area was measured by means of the photogrammetric method using unmanned aerial vehicles (UAVs) and, for comparison, by means of the precise positioning method using the GNSS system with the RTN kinematic technique (GNSS RTN). Publicly available measurement data from airborne laser scanning (LIDAR) were also used for the analyses. In order to assess the accuracy of the methods employed, the discrete data describing the relief were analysed, and then continuous data in raster form were analysed. The analyses were performed with the use of an automatic method of height comparison and analysis of cross-sections on DEM and DSM. In the last stage of the work, the hydrological parameters were analysed, i.e., the slope of the land and the watercourse flow values determined based on the DEM captured from various surveying methods. What is unique about this work is: (1) the use of spatial data with a high resolution acquired from UAVs to search for locations for the construction of small hydroelectric power plants and assessment of their usefulness in this regard; (2) assessment of the accuracy and quality of the hydrological and morphological parameters of the area important for the selection of the location for the construction of small hydropower plants, performed based on spatial data depicting the actual area of the land measured in the field; (3) assessment of the quality of the hydrological modelling necessary to find the location. The performed surveys and analyses allowed for the identification of the advantages and disadvantages of the surveying methods employed to capture data on the topography of land as well as indications of the optimal data source.

/pdf/assessing-validity-of-employing-surveying-methods-to-capture-2luno8qz.pdf

Assessing Validity of Employing Surveying Methods to Capture Data on Topography to Determine Hydrological and Topographic Parameters Essential for Selecting Locations for the Construction of Small Hydropower Plants

Application of artificial neural network and Soil and Water Assessment Tools in evaluating power generation of small hydropower stations

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Application of artificial neural network and Soil and Water Assessment Tools in evaluating power generation of small hydropower stations