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Landslides pose significant risks to human lives and infrastructure. The Medea region in Algeria is particularly susceptible to these destructive events, which result in substantial economic losses. Despite this vulnerability, a comprehensive landslide map for this region is lacking. This study aims to develop a novel hybrid metaheuristic model for the spatial prediction of landslide susceptibility in Medea, combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) with four novel optimization algorithms (Genetic Algorithm—GA, Particle Swarm Optimization—PSO, Harris Hawks Optimization—HHO, and Salp Swarm Algorithm—SSA). The modeling phase was initiated by using a database comprising 160 landslide occurrences derived from Google Earth imagery; field surveys; and eight conditioning factors (lithology, slope, elevation, distance to stream, land cover, precipitation, slope aspect, and distance to road). Afterward, the Gamma Test (GT) method was used to optimize the selection of input variables. Subsequently, the optimal inputs were modeled using hybrid metaheuristic ANFIS techniques and their performance evaluated using four relevant statistical indicators. The comparative assessment demonstrated the superior predictive capabilities of the ANFIS-HHO model compared to the other models. These results facilitated the creation of an accurate susceptibility map, aiding land use managers and decision-makers in effectively mitigating landslide hazards in the study region and other similar ones across the world. [ABSTRACT FROM AUTHOR]

Accurately estimating evaporation is essential for water managers to formulate effective rules and policies. The complexity arising from intricate interactions within the soil-atmosphere system makes evaporation a challenging parameter to predict. In the past decade, Machine Learning techniques rooted in soft computing have emerged as potent tools for addressing the intricacies and non-linearities in hydrology. Surprisingly, there has been no prior research exploring the impact of reservoir water temperature on evaporation modeling. Consequently, this study aimed to employ a hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with four optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Salp Swarm Algorithm (SSA). The focus was on modeling the monthly evaporation of the Boukourdane Dam in Algeria for the period of September 1996 to August 2016 and examining how the reservoir's water temperature influences model performance based on four performance indicators, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), scatter index (SI) and Correlation Coefficient (R). The findings underscored the significance of incorporating the water temperature parameter. Among the models, ANFIS-HHO demonstrated the highest accuracy with MAE, RMSE and R values of 1.28, 1.21 mm and 0.92, respectively during test period. Moreover, results revealing a notable impact of reservoir water temperature on evaporation forecasting and the addition of this parameter provide an increase in R and decrease in RMSE about 4.54% and 17.98% respectively. [ABSTRACT FROM AUTHOR]

In this research, the role of climate variability and weather change in short-term streamflows, including extreme event, was investigated in semiarid climates. The deep learning convolutional neural networks (CNN) were modified by incorporating the imperialist competitive algorithm (ICA) and the grey wolf optimizer (GWO) method to improve hourly runoff predictions at multiple scales, ranging from 100 to over 6,000 km2 in the Seybouse Basin, Algeria. The atmospheric reanalysis data set, ECMWF Reanalysis v5 (ERA5) with a 31-km resolution, climate variability indices, and in situ runoff observations were used. The most relevant atmospheric and soil moisture predictors from the reanalysis grids covering the study area were used to represent spatial variability. The prediction performance of the original CNN and modified CNN-ICA and CNN-GWO models were evaluated. The CNN-GWO model outperformed CNN-ICA and the original model in predicting runoff and improved the Nash-Sutcliffe Efficiency score up to 0.99. Results across multiple scales disclose that the models with climate indices as inputs showed higher performance than the models with only atmospheric data as inputs, especially in predicting extreme runoff values in basins with elevations above 670 m, suggesting that climate variability indices need to be considered in flood predictions and infrastructure design in mountainous areas with increasing climate change uncertainties. [ABSTRACT FROM AUTHOR]

Among the existing geophysical methods, the vertical electrical sounding remains a fast and economical way to detect groundwater resources. However, the interpretation of the vertical electrical sounding data often suffers from non‐uniqueness due to the ill‐posed nature of the inverse problem. In recent years, metaheuristic algorithms have been successfully used for solving ill‐conditioned and ill‐posed problems. This work presents a scheme that uses the continuous ant colony optimization (ACOR) technique to invert vertical electrical sounding data. The ACOR is a global search algorithm that explores and finds the globally optimal solution over a search space by mimicking the behaviour of biological ants. The development of this algorithm was due to the requirement to interpret a set of vertical electrical sounding collected at the region of Hassi R'Mel (Algerian Sahara). The area has a particular geological/geoelectrical structure, which renders the interpretation of vertical electrical sounding challenging as standard inversion approaches tend to fail to recover a reliable resistivity model. The ACOR algorithm was initially tested with synthetic data from models simulating the geological/hydrogeological structure of the studied area. The results verified the robustness and stability of the ACOR algorithm even in the presence of a high level of noise. Furthermore, the tests indicated that the ACOR algorithm performed better when compared to other inversion techniques for this particular geoelectrical structure. Five vertical electrical sounding profiles using a Schlumberger array collected in the region of Hassi R'Mel were inverted using the ACOR algorithm. The models confirmed the presence of the two central aquifer systems and showed the geometry of the aquifer with the most favourable conditions for water accumulations. [ABSTRACT FROM AUTHOR]

• An autonomous water pumping system (WPS) is suggested. • A hybrid energy system is optimized through seagull optimization algorithm (SOA). • The impacts of load management (LM) strategy in system sizing is analyzed. • Techno-economic evaluation in the presence and absence of load management is made. This study introduces an innovative irrigation system tailored for a tomato field in Algeria, optimizing its elements using Seagull Optimization Algorithm (SOA) over a 20-year lifespan. The system integrates photovoltaic (PV), wind turbine (WT), diesel generator (DG), battery, and tank storage for water pumping. The novelty lies in the incorporation of Load Management (LM), creating a dynamic water demand profile across four tomato development stages. Without LM, the optimal configuration yields a Net Total Present Cost (NTPC) of $14,140, corresponding to a Cost of Energy (COE) of 0.292 $/kWh. This configuration ensures a 24% Loss of Power Supply Probability (LPSP), a 41.2 % Loss of Water Supply Probability (LWSP), and an emission factor of 0.169 kg/kWh during 279 h of DG operation annually. With LM, the COE drops to $0.298/kWh, reducing the NTPC to $12,080. This LM strategy gives an 18 % LPSP, 20 % LWSP, and an Energy Fraction (EF) of 0.140 kg/kWh during 168 h of DG operation annually. The present application is a promising and cost-effective solution for optimizing Water Pumping Systems (WPS) in agricultural settings. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference evapotranspiration (ETo) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation losses. Therefore, ETo-based estimation is a major concern in the hydrological cycle. The estimation of ETo can be determined using various methods, including field measurement (the scale of the lysimeter), experimental methods, and mathematical equations. The Food and Agriculture Organization recommended the Penman-Monteith (FAO-56 PM) method which was identified as the standard method of ETo estimation. However, this equation requires a large number of measured climatic data (maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) that are not always available on meteorological stations. Over the decade, the artificial intelligence (AI) models have received more attention for estimating ETo on multi-time scales. This research explores the potential of new hybrid AI model, i.e., support vector regression (SVR) integrated with grey wolf optimizer (SVR-GWO) for estimating monthly ETo at Algiers, Tlemcen, and Annaba stations located in the north of Algeria. Five climatic variables namely relative humidity (RH), maximum and minimum air temperatures (Tmax and Tmin), solar radiation (Rs), and wind speed (Us) were used for model construction and evaluation. The proposed hybrid SVR-GWO model was compared against hybrid SVR-genetic algorithm (SVR-GA), SVR-particle swarm optimizer (SVR-PSO), conventional artificial neural network (ANN), and empirical (Turc, Ritchie, Thornthwaite, and three versions of Valiantzas methods) models by using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI), and through graphical interpretation. Through the results obtained, the performance of the SVR-GWO provides very promising and occasionally competitive results compared to other data-driven and empirical methods at study stations. Thus, the proposed SVR-GWO model with five climatic input variables outperformed the other models (RMSE = 0.0776/0.0613/0.0374 mm, NSE = 0.9953/ 0.9990/0.9995, PCC = 0.9978/0.9995/0.9998 and WI = 0.9988/0.9997/0.9999) for estimating ETo at Algiers, Tlemcen, and Annaba stations, respectively. In conclusion, the results of this research indicate the suitability of the proposed hybrid artificial intelligence model (SVR-GWO) at the study stations. Besides, promising results encourage researchers to transfer and test these models in other locations in the world in future works. [ABSTRACT FROM AUTHOR]

This paper presents the use of a novel nature inspired meta-heuristic algorithm namely Ant Lion Optimizer (ALO), which is inspired from the ant lions hunting mechanism to enhance the frequency regulation and optimize the load frequency control (LFC) loop parameters. The frequency regulation issue was formulated as an optimal load frequency control problem (OLFC). The proposed ALO algorithm was applied to reach the best combination of the PID controller parameters in each control area to achieve both frequency and tie-line power flow exchange deviations minimization. The control strategy has been tested firstly with the standard two-area power system, followed by the IEEE three-area Western System Coordinating Council (WSCC) and, lastly, with the large three-area South-Western part of the Mediterranean interconnected power system (SWM): Tunisia, Algeria and Morocco. The dynamic performances of the test systems are compared to other approaches available in literature. The simulation results of this research show that ALO algorithm is able to solve LFC problem and achieve less frequency and tie-line power flow deviations than those determined by other methods used in this paper. [ABSTRACT FROM AUTHOR]

This paper is intended to solve the optimal power flow (OPF) dispatch in the presence of wind power generation (WPG) in the Adrar power system. Towards this aim, the performances of three powerful meta-heuristic algorithms-namely, the cuckoo search algorithm (CSA), firefly algorithm (FFA), and flower pollination algorithm (FPA) are investigated. The proposed algorithms are applied to best capture the active power produced with the minimum value of a multi-objective function. This latter includes: the fuel cost, the NOx emissions, and the imbalance cost of the WPGs. Furthermore, considering the uncertainties governing wind resources, the maximum wind power output is estimated using the wind speed carrying maximum energy. It was found that all algorithms perform well in providing accurate solutions. Interestingly, the convergence is reached in the first 135 iterations. A remarkable outcome of the present work is that CSA outperforms FPA and FFA. CSA has proved itself to be a great tool to optimize Adrar's power flow system in term of iterations and computational time. [ABSTRACT FROM AUTHOR]

Summary: In this article, an optimal design model using metaheuristic techniques to overcome such difficult optimization problems has been proposed. For this purpose, various well‐known metaheuristic algorithms have been used, namely: ant lion optimizer (ALO), bat algorithm (BA), cuckoo search algorithm (CSA), and gray wolf optimization algorithm (GWO). To provide optimal system configurations, an objective function based on the total net present cost (TNPC) has been specified while taking into account a number of constraints, including the loss of power supply probability (LPSP). The effectiveness of the used algorithms in solving such problem is assessed and their performances compared with each other. To assess the suggested model's effectiveness, a case study has been conducted to investigate a stand‐alone photovoltaic water pumping system (PVWPS), designed to supply water to a one‐hectare field of date palm trees. This field of an oasis located in the Ghardaïa region of Algeria consists of 130 date palm trees spaced 9 m apart. The simulation results show the effectiveness of the GWO algorithm over the other algorithms. GWO converges toward the optimal solutions for different heads and all reliability levels required by the customer. Another aspect, supported by the developed methodology, is the accounting of the excess water volume. It has been found that the relative surplus is inversely proportional to the PVWPS reliability level. For a head of 30 m, the relative excess is estimated at 120% for LPS0%, while it is 27% for LPSP of 10%. The same observations are made for the other heads. [ABSTRACT FROM AUTHOR]