A new pre-processing methodology for gridded Satellite Precipitation Products (SPPs) is developed to improve the performance of Machine Learning (ML) algorithms for runoff prediction. The developed ...approach was applied to capture the rainfall patterns, and to select relevant input data. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modelling. The methodology was tested in a semiarid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPMIMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. The results demonstrate the effectiveness of the proposed approach using all employed SPPs. In terms of Nash-Sutcliffe efficiency, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and of ELM by 15%, which highlights how pre-processing techniques significantly enhance ML models with SPP data.
Madjez Ressoul catchment constitutes an important source of fresh water and arable land in northeastern Algeria. In order to achieve better management of the catchments’ natural resources, ...specifically water, an advanced flood recession analysis was conducted, using the recession analysis-based trigonometric approach, which was based completely on a mathematical solution. This approach provides very useful results for the master recession curves construction. The advantage of this method in the hydrograph separation is both its non-subjectivity related to the user, and then its viability for initial use in the hydrograph separation field. Results in this real case give a better indication of groundwater flow during different drought periods, using many assessed parameters of initial discharge and relative recession time. A particular review of existing hydrograph separation techniques is used to situate the recession analysis and show its case of application relative to other techniques.
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.
Evaluation and modeling of soil water infiltration are essential to all aspects of water resources management and the design of hydraulic structures. Nonetheless, research focused on experimental ...studies of infiltration rates in arid and semi-arid regions under unknown boundary conditions remains minimal. This paper investigates the characteristics of the spatial variability of infiltration over a semi-arid rural basin of Algeria. The experiments were conducted using a portable double-ring infiltrometer filled at an equal volume of approximately 100 L of water for each of the 25 catchment locations. Soil moisture contents at the proximity of each test location were evaluated in the laboratory as per the standard NF P94–050 protocol. The experimental results are used to produce the catchment infiltration curves using three statistically fitted infiltration models, namely Horton, Kostiakov, and Philip models. The reliability of the models was assessed using four performance criteria. The statistical regressions of the fitted models suggest that the Horton model is the most suitable to assess the infiltration rate over the catchment with mean coefficients of Nash = 0.963, CC = 0.985, RMSE = 1.839 (cm/h), and Bias = 0.241. The superiority of the Horton model suggests that the initial and final infiltration rates, primarily affected by soil type, initial soil moistures, and land cover, are important predictors of the modeling process over the Madjez Ressoul catchment. The results also infer that the applicability of other models to the different types of undeveloped soils in the study area requires advanced field investigations. This finding will support the understanding of the hydrologic processes over semi-arid basins, especially in advising crop irrigation schemes and methods and managing the recurring flood and drought over the country.
In this paper, we introduce a new approach, based on a unified framework incorporating Data Envelopment Analysis (DEA) and Ordered Weighted Averaging (OWA), for assessing water quality in contextual ...settings that involve a large number of hydrochemical parameters. In order to enhance discrimination among water sources, the DEA model is adopted with data-driven input variables, called “surrogate optimistic closeness values,” computed through an aggregation procedure that includes the observed values of the hydrochemical parameters with OWA weights. The proposed DEA-OWA methodology has been employed to assess the quality of 51 water samples, collected from irrigation wells in Sereflikochisar Basin, Turkey, by means of 19 hydrochemical parameters. Using different subjectivity levels, the Surrogate Water Quality Indices (SWQIs) that are produced are proven effective in enhancing discrimination among the water sources while enabling a more robust water quality-based ranking. The k-means analysis has been used for clustering the water quality of the wells into Excellent, Good, Permissible, and Unsuitable rather than using pre-set boundaries. Only one water source has been identified as Excellent, whereas 17.65%, 45.10%, and 35.29% of the sampled wells, respectively, are categorized with Good, Permissible, and Unsuitable water quality. Inferred from wells’ location, the results suggest that the groundwater might be drastically affected by saline water intrusion from Lake Tuz. The latter conclusion has been corroborated through a Tobit regression analysis.
The peri-urban catchments are distinguished by discontinuous urban extensions. They extend between the margins of the city and the borders of the rural space forming a mid-urban, mid rural mosaic. ...They experience unprecedented expansion movement since the end of the 60 years. When hydrological models for urban and for rural catchments have been developed, it was until recently impossible to apply those principles in a concrete manner to peri-urban catchments. The representation of the hydrological functioning of these surfaces can be done by considering both urban processes and rural processes. In this paper, we present the simulation model named “Multi-Outlets model”. This model allows taking into account the mixed nature of peri-urban areas. The model was applied to Yzeron catchment located in Lyon, France.
Empirical models of recession analysis provide information about surface and ground flow processes during periods of drought. The objective of this research is to evaluate the performance of three ...different techniques (individual segments, filtering method, genetic algorithms) in assessing the contribution of recession flow component in calculating runoff, using daily records of discharge. These techniques will focus on the values of the recession index covering ten flood events in the period of 1973–2003. The outputs of these models were then compared and their results appeared to validate two particular techniques: the filtering method with one parameter and the genetic algorithm method. The coefficient of determination (
R
2
= 0.7324–0.935) and the cross probability which equals 0.9 confirm the best separation of all events. Absolute similarities between flow types in the filtering method and genetic algorithms present systematic differences in the calibration form and on the consideration of obstacles and limitations.
Climate change impacts affect the hydrological cycle and hence the availability of water resources and their management. Rainfall, the most important hydro-meteorological event and as the main source ...of water, may have increasing or decreasing trends depending on geography and location, general air circulation, proximity to coastal areas, and geomorphology. There are many studies using monotonic trend analysis in the literature, but it is important to assess these trends at different levels for proper recording. For this purpose, in this paper, instead of using monotonic trend analysis, partial trends will be sought at “Low,” “Medium,” and “High” rainfall records groups, which is possible through the innovative trend analysis (ITA) methodology. Algeria being adjacent to the Mediterranean Sea is impacted by variations in rainfall. The application of the ITA methodology is presented for 16 different Algerian annual rainfall records from 1982 to 2019 in the north-eastern region of the country which is in proximity to the Mediterranean basin. Partially increasing, decreasing, or no trend pieces are identified at each station. It is concluded as the future unfolds some stations will record dry spell or drought dangers for “Low” data groups, and significant flood danger for the “High” rainfall amount data group. In general, the study area is known to be subject to an increasing rainfall trend. This is due to the mountainous terrain in the study area and makes for confrontation with cold air movements from the European continent during winter periods.
In this paper, we use an integrated approach to carry out a comprehensive evaluation of water quality in the Beni Haroun (BH) dam, the largest surface water resource in Algeria. Several techniques ...have been employed under the same framework, including the Canadian Council Ministers Environment Water Quality Index (CCME-WQI), principal component analysis and factor analysis (PCA/FA), the K-means clustering, and the ordinary least square (OLS) analysis. A data set of 22 physicochemical parameters has been collected, over a period of 11 years, from three sampling stations: Ain Smara (ST1) and Menia (ST2), both located upstream of “Wadi Rhumel,” and BH dam station (ST3), located at the dam site. The PCA/FA enables the identification of seven key factors that influence significantly BH dam water quality. The average values of CCME indices at the BH dam were 17, 40, 42, and 32 for drinking, irrigation, industry, and aquatic life purposes, respectively, which indicate poor water quality, according to the CCME categorization scheme. Besides, the K-means algorithm has been proven to be a very useful machine learning tool to detect that the major source of BH dam pollution is “Wadi Rhumel.” Finally, OLS analysis, along with the Mann-Kendall test, highlighted the positive trend of BH dam’s water quality.
Static and free vibration analyses of a straight beam with four general boundary conditions on elastic foundation are performed. In this study, the effects of beam material, boundary conditions, beam ...geometry (cross-sectional area), and inertia moment on natural frequencies and maximal displacements in free vibration were experimentally investigated. Optimal vibration conditions for each performance level are established, and the relationship between the variables and the physical parameters is determined using a quadratic regression model. The results show that the natural frequency is influenced principally by the boundary conditions and in the second level by the beam material. Also, it is indicated that the beam material is the dominant parameter affecting maximal displacement.
