Precise solar radiation forecasting can provide great benefits and solutions for smart grid distribution and electricity management. However, its non-stationary behavior and randomness render its ...estimation very difficult. In this respect, a new hybrid learning approach is proposed for multi-hour global solar radiation forecasting, relying on Convolutional Neural Network (CNN), Nonparametric Gaussian Process Regression (GPR), Least Support Vector Machine (LS-SVM), and Extreme Learning Machine (ELM) as essence predictors. Then compressive sensing technique is applied to perform a hybridization scheme of the model’s output. Hourly global solar radiation data from two sites in Algeria with different climate conditions are used to evaluate the full potential of the integrated model, with stationarity checks with an advanced clear sky model (MecClear model). Different comparative simulations show the superiority of the proposed pipeline in forecasting hourly global solar radiation data for multi-hour ahead compared to the stand-alone model. Experimental results show that the proposed hybridization methodology can effectively improve the prediction accuracy and outperforms benchmarking models during all the forecasting horizons.
This study presents two machine learning models, namely, the light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood ...susceptibility (FFS) in the Wadi System (Hurghada, Egypt). A flood inventory map with 445 flash flood sites was produced and randomly divided into two groups for training (70%) and testing (30%). Fourteen flood controlling factors were selected and evaluated for their relative importance in flood occurrence prediction. The performance of the two models was assessed using various indexes in comparison to the common random forest (RF) method. The results show areas under the receiver operating characteristic curves (AUROC) of above 97% for all models and that LightGBM outperforms other models in terms of classification metrics and processing time. The developed FFS maps demonstrate that highly populated areas are the most susceptible to flash floods. The present study proves that the employed algorithms (LightGBM and CatBoost) can be efficiently used for FFS mapping.
Abstract
Urban sewer system management is challenging due to its higher vulnerability to flooding caused by rapid urbanization and climate change. For local decision-makers, storm water management is ...essential for urban planning and development. Therefore, the main objective of this study is to develop a numerical model for the sewerage network of the central catchment area of Algiers since it has experienced frequent overflows during the winter season. For this purpose, to model the sewerage networks, the model was built by coupling ArcGIS with MIKE URBAN. Its calibration and validation were performed using real-time measurements with a time step of 15 min. The model was evaluated by several statistical indicators, such as the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). The model results showed acceptable model performance, with an NSE superior to 0.50, R2 of approximately 0.63, RMSE of 7%, and PBIAS of 10% during the validation of the model. The performance parameters prove the reliability of the developed model. The employed model can be applied in other regions and could be helpful for policymakers and managers to improve flood mitigation measures based on the model prediction of the sewerage network.
This study aims to examine three machine learning (ML) techniques, namely random forest (RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu Gia-Thu Bon (VGTB). ...The results of ML are compared with those of the rainfall-runoff model, and different training dataset sizes are utilized in the performance assessment. Ten independent factors are assessed. An inventory map with approximately 850 flooding sites is based on several post-flood surveys. The inventory dataset is randomly split between training (70%) and testing (30%). The AUC-ROC results are 97.9%, 99.5%, and 99.5% for CatBoost, LightGBM, and RF, respectively. The FSMs developed by the ML methods show good agreement in terms of an extension with flood inundation maps developed using the rainfall-runoff model. The models' FSMs showed 10-13% of the total area to be highly susceptible to flooding, consistent with RRI's flood map. The FSMs show that downstream areas (both urbanized and agricultural) are under high and very high levels of susceptibility. Additionally, different sizes of the input datasets are tested to determine the least number of data points having acceptable reliability. The results demonstrate that the ML methods can realistically predict FSMs, regardless of the number of training samples.
Predicting flash flood-prone areas is essential for proactive disaster management. However, such predictions are challenging to obtain accurately with physical hydrological models owing to the ...scarcity of flood observation stations and the lack of monitoring systems. This study aims to compare machine learning (ML) models (Random Forest, Light, and CatBoost) and the Personal Computer Storm Water Management Model (PCSWMM) hydrological model to predict flash flood susceptibility maps (FFSMs) in an arid region (Wadi Qows in Saudi Arabia). Nine independent factors that influence FFSMs in the study area were assessed. Approximately 300 flash flood sites were identified through a post-flood survey after the extreme flash floods of 2009 in Jeddah city. The dataset was randomly split into 70 percent for training and 30 percent for testing. The results show that the area under the receiver operating curve (ROC) values were above 95% for all tested models, indicating evident accuracy. The FFSMs developed by the ML methods show acceptable agreement with the flood inundation map created using the PCSWMM in terms of flood extension. Planners and officials can use the outcomes of this study to improve the mitigation measures for flood-prone regions in Saudi Arabia.
Medjerda is a key transboundary watershed in the Maghreb region, crossing from the Algerian mountains through northern Tunisia. Therefore, the analysis of the rainfall regime in this basin is of ...paramount importance for water resources management and regional economic development, notably concerning agriculture. This study examines the rainfall trends over the Medjerda watershed on multi-temporal scales (monthly, seasonally and annually) with a database of monthly rainfall observed in 60 stations evenly spread over the watershed. After filling gaps and homogenizing data, the Mann–Kendall test for trend detection was applied to rainfall series and the Sen’s slope method was adopted to estimate the trend’s magnitude, interpolated over the sub-catchments, to analyze the spatial distribution of rainfall changes within the watershed. Results showed the absence of significant trends at the annual scale for the entire catchment. However, rainfall redistribution was observed throughout the year, with a notable precipitation reduction during spring and increased winter precipitation, which could impact agriculture and ecosystem functioning. This modification of the rainfall regime implies an adaptation of the management of dams and reservoirs, with a reduced filling capacity during spring in anticipation of the summer dry season.
Integrating photovoltaic power into the power system can offer significant economic and environmental benefits. However, the intermittent and random nature of photovoltaic power generation poses a ...challenge to the current power system's planning and operation. Accurate photovoltaic power generation forecasting is crucial for delivering high-quality electric energy to consumers and increasing system reliability.In this study, a multi-stage approach is proposed. In the first stage, three decomposition techniques (Iterative Filtering decomposition, Variational Mode Decomposition, and Wavelet Packet Decomposition) are employed for time series decomposition. Then, for each Intrinsic Mode Function (IMF) component resulting from the decomposition block, five machine learning and three deep learning algorithms are utilized, serving as local forecasting models. In the final forecast phase, the best forecasting result for each regressor is selected during the reconstruction phase. Two years of photovoltaic power data recorded in three grid-connected photovoltaic systems installed in South Algeria were utilized for training and testing the proposed forecasting models. Upon comprehensive analysis and examination of the outcomes, the proposed method exhibits the lowest normalized Root Mean Square Error values across all forecast horizons and monitoring stations. Particularly, for forecasting steps at time intervals +1, +3, and +5, the proposed method attains an average normalized Root Mean Square Error, showcasing its efficacy: 0.709%, 2.097%, and 3.241% for station 1; 1.147%, 3.546%, and 5.347% for station 2; and 0.922%, 2.158%, and 4.539% for station 3. The experimental results underscore the superiority of our approach over conventional regression algorithms, thus substantiating its prowess in delivering robust and competitive performance outcomes.
In arid and semi-arid areas where desertification is progressing, exploitation of water resources is focused on surface water and groundwater who pose sustainability problem. The exploitation of ...surface water uses “surface dam” heavily exposed to evaporation and cannot perform the function of “dam-reservoir” in the dry season. We envision the use of groundwater by the underground dams, designed to contain groundwater and accumulate water.
Compared with conventional dam, underground dams have the advantage of not overwhelm the land, offer particularly low evaporation losses and preserve the environment. A DEM and the river network of the study area were obtained by ArcGIS software The results obtained were injected into Modflow to extract the discharge. The dam body stability has been verified and validated using a model of the fluid-structure interaction under the platform “COMSOL Multiphysics”.
A significant gain in water volume is found by storing away the long intense sunshine and evaporation. COMSOL allows us to have an optimal design of the dam body.The development of the region requires necessary a control of water resources.
The purpose of this study is to evaluate the quality of groundwater used for irrigating agricultural areas in the region of Ouargla by using several quality indices and to map the spatial ...distribution of these indices. This spatial mapping will help create a model for the classification of these resources according to their suitability for irrigation. For this purpose, physicochemical analyses were carried out on samples from 38 wells distributed over the entire territory of the studied region. These include 27 wells that capture Mio-Pliocene groundwater (Terminal Complex) and 11 wells in the Albian groundwater (Intercalary Continental). The thematic maps were developed using a geographic information system (GIS). In this study, we used the following eight indices: potential salinity (PS); residual sodium carbonate (RSC); electrical conductivity (EC); magnesium percent (%Mg2+); sodium percent (%Na+); permeability index (PI); Kelley’s ratio (KR); sodium adsorption ratio (SAR). The global qualitative study of the water used for irrigation shows that these resources fall into three categories (good, permissible, and poor for irrigation). Water quality analysis shows that, based on the magnesium percent (%Mg2+), 18% of the wells can be considered to be of good quality, 74% of the wells are of medium quality (permissible for irrigation), and 8% are of poor quality (unsuitable for irrigation). According to the Wilcox diagram, the waters used in the Ouargla region are of very poor quality. Very excessive mineralization, expressed by the electrical conductivity, was observed for the Mio-Pliocene and Albian waters, where it varies with values in the range of 2340 µS/m to 6520 µS/m and 2330 µS/m to 3840 µS/m, respectively. This conductivity presents a high risk of alkalinization.
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