Artificial neural network models for estimating regional reference evapotranspiration based on climate factors

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, an artificial neural network (ANN) model for reference evapotranspiration (ET₀) calculation was investigated. ANNs were trained and tested for arid (west), semi‐arid (middle) and sub‐humid (east) areas of the Inner Mongolia district of China. Three or four climate factors, i.e. air temperature (T), relative humidity (RH), wind speed (U) and duration of sunshine (N) from 135 meteorological stations distributed throughout the study area, were used as the inputs of the ANNs. A comparison was conducted between the estimates provided by the ANNs and by multilinear regression (MLR). The results showed that ANNs using the climatic data successfully estimated ET₀ and the ANNs simulated ET₀ better than the MLRs. The ANNs with four inputs were more accurate than those with three inputs. The errors of the ANNs with four inputs were lower (with RMSE of 0·130 mm d^?1, RE of 2·7% and R² of 0·986) in the semi‐arid area than in the other two areas, but the errors of the ANNs with three inputs were lower in the sub‐humid area (with RMSE of 0·21 mm d^?1, RE of 5·2% and R² of 0·961. For the different seasons, the results indicated that the highest errors occurred in September and the lowest in April for the ANNs with four inputs. Similarly, the errors were higher in September for the ANNs with three inputs. Copyright © 2008 John Wiley & Sons, Ltd.     

Performance evaluation of satellite-based approaches for the estimation of daily air temperature and reference evapotranspiration

Different satellite-based radiation (Makkink) and temperature (Hargreaves-Samani, Penman-Monteith temperature, PMT) reference evapotranspiration (ET_o) models were compared with the FAO56-PM method over the Cauvery basin, India. Maximum air temperature (T_max) required in the ET_o models was estimated using the temperature–vegetation index (TVX) and an advanced statistical approach (ASA), and evaluated with observed T_max obtained from automatic weather stations. Minimum air temperature (T_min) was estimated using ASA. Land surface temperature was employed in the ET_o models in place of air temperature (T_a) to check the potency of its applicability. The results suggest that the PMT model with T_a as input performed better than the other ET_o models, with correlation coefficient (r), averaged root mean square error (RMSE) and mean bias error (MBE) of 0.77, 0.80 mm d^?1 and ?0.69 for all land cover classes. The ASA yielded better T_max and T_min values (r and RMSE of 0.87 and 2.17°C, and 0.87 and 2.27°C, respectively).     

Trends in reference evapotranspiration in the humid region of northeast India

In the present study, the trends in the reference evapotranspiration (ET_O) estimated through the Penman‐Monteith method were investigated over the humid region of northeast (NE) India by using the Mann‐Kendall (MK) test after removing the effect of significant lag‐1 serial correlation from the time series of ET_O by pre‐whitening. During the last 22 years, ET_O has been found to decrease significantly at annual and seasonal time scales for 6 sites in NE India and NE India as a whole. The seasonal decreases in ET_O have, however, been more significant in the pre‐monsoon season, indicating the presence of an element of a seasonal cycle. The decreases in ET_O are mainly attributed to the net radiation and wind speed, which are also corroborated by the observed trends in these two parameters at almost all the times scales over most of the sites in NE India. The steady decrease in wind speed and decline in net radiation not only balanced the impact of the temperature increases on ET_O, but may have actually caused the decreases in ET_O over the humid region of northeast India. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth–Wallace?

Potential evapotranspiration (PET) is a key input to hydrological models. Its estimation has often been via the Penman–Monteith (P–M) equation, most recently in the form of an estimate of reference evapotranspiration (RET) as recommended by FAO‐56. In this paper the Shuttleworth–Wallace (S–W) model is implemented to estimate PET directly in a form that recognizes vegetation diversity and temporal change without reference to experimental measurements and without calibration. The threshold values of vegetation parameters are drawn from the literature based on the International Geosphere–Biosphere Programme land cover classification. The spatial and temporal variation of the LAI of vegetation is derived from the composite NOAA‐AVHRR normalized difference vegetation index (NDVI) using a method based on the SiB2 model, and the Climate Research Unit database is used to provide the required meteorological data. All these data inputs are publicly and globally available. Consequently, the implementation of the S–W model developed in this study is applicable at the global scale, an essential requirement if it is to be applied in data‐poor or ungauged large basins. A comparison is made between the FAO‐56 method and the S–W model when applied to the Yellow River basin for the whole of the last century. The resulting estimates of RET and PET and their association with vegetation types and leaf area index (LAI) are examined over the whole basin both annual and monthly and at six specific points. The effect of NDVI on the PET estimate is further evaluated by replacing the monthly NDVI product with the 10‐day product. Multiple regression relationships between monthly PET, RET, LAI, and climatic variables are explored for categories of vegetation types. The estimated RET is a good climatic index that adequately reflects the temporal change and spatial distribution of climate over the basin, but the PET estimated using the S–W model not only reflects the changes in climate, but also the vegetation distribution and the development of vegetation in response to climate. Although good statistical relationships can be established between PET, RET and/or climatic variables, applying these relationships likely will result in large errors because of the strong non‐linearity and scatter between the PET and the LAI of vegetation. It is concluded that use of the implementation of the S–W model described in this study results in a physically sound estimate of PET that accounts for changing land surface conditions. Copyright © 2006 John Wiley & Sons, Ltd.     

Polynomial chaos expansion and response surface method for nonlinear modelling of reference evapotranspiration

The feasibility of polynomial chaos expansion (PCE) and response surface method (RSM) models is investigated for modelling reference evapotranspiration (ET₀). The modelling results of the proposed models are validated against the M5 model tree and multi-layer perceptron neural network (MLPNN) methods. Two meteorological stations, Isparta and Antalya, in the Mediterranean region of Turkey, are inspected. Various input combinations of daily air temperature, solar radiation, wind speed and relative humidity are constructed as input attributes for the ET₀. Generally, the modelling accuracy is increased by increasing the number of inputs. Including wind speed in the model inputs considerably increases their accuracy in modelling ET₀. Mean absolute error (MAE), root mean square error (RMSE), agreement index (d) and Nash-Sutcliffe efficiency (NSE) are used as comparison criteria. The PCE is the most accurate model in estimating daily ET₀, giving the lowest MAE (0.036 and 0.037 mm) and RMSE (0.047 and 0.050 mm) and the highest d (0.9998 and 0.9999) and NSE (0.9992 and 0.9996) with the four-input PCE models for Isparta and Antalya, respectively.     

Analysis of trends in reference evapotranspiration data in a humid climate

Abstract

Statistically significant FAO-56 Penman-Monteith (FAO-56 PM) and adjusted Hargreaves (AHARG) reference evapotranspiration (ET₀) trends at monthly, seasonal and annual time scales were analysed by using linear regression, Mann-Kendall and Spearman’s Rho tests at the 1 and 5% significance levels. Meteorological data were used from 12 meteorological stations in Serbia, which has a humid climate, for the period 1980–2010. Web-based software for conducting the trend analyses was developed. All of the trends significant at the 1 and 5% significance levels were increasing. The FAO-56 PM ET₀ trends were almost similar to the AHARG trends. On the seasonal time scale, for the majority of stations significant increasing trends occurred in summer, while no significant positive or negative trends were detected by the trend tests in autumn for the AHARG series. Moreover, 70% of the stations were characterized by significant increasing trends for both annual ET₀ series.

Editor Z.W. Kundzewicz; Associate editor S. Grimaldi

Citation Gocic, M. and Trajkovic, S., 2013. Analysis of trends in reference evapotranspiration data in a humid climate. Hydrological Sciences Journal, 59 (1), 165–180.     

Prediction of hourly actual evapotranspiration using neural networks,genetic programming,and statistical models

The complexity of the evapotranspiration process and its variability in time and space have imposed some limitations on previously developed evapotranspiration models. In this study, two data‐driven models: genetic programming (GP) and artificial neural networks (ANNs), and statistical regression models were developed and compared for estimating the hourly eddy covariance (EC)‐measured actual evapotranspiration (AET) using meteorological variables. The utility of the investigated data‐driven models was also compared with that of HYDRUS‐1D model, which makes use of conventional Penman–Monteith (PM) model for the prediction of AET. The latent heat (LE), which is measured using the EC method, is modelled as a function of five climatic variables: net radiation, ground temperature, air temperature, relative humidity, and wind speed in a reconstructed landscape located in Northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg–Marquardt and Bayesian regularization. The GP technique was used to generate mathematical equations correlating AET to the five climatic variables. Furthermore, the climatic variables, as well as their two‐factor interactions, were statistically analysed to obtain a regression equation and to indicate the climatic factors having significant effect on the evapotranspiration process. HYDRUS‐1D model as an available physically based model was examined for estimating AET using climatic variables, leaf area index (LAI), and soil moisture information. The results indicated that all three proposed data‐driven models were able to approximate the AET reasonably well; however, GP and regression models had better generalization ability than the ANN model. The results of HYDRUS‐1D model exhibited that a physically based model, such as HYDRUS‐1D, might be comparable or even inferior to the data‐driven models in terms of the overall prediction accuracy. Based on the developed GP and regression models, net radiation and ground temperature had larger contribution to the AET process than other variables. Copyright © 2010 John Wiley & Sons, Ltd.     

Estimation of monthly evaporative loss using relevance vector machine,extreme learning machine and multivariate adaptive regression spline models

The forecasting of evaporative loss (E) is vital for water resource management and understanding of hydrological process for farming practices, ecosystem management and hydrologic engineering. This study has developed three machine learning algorithms, namely the relevance vector machine (RVM), extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) for the prediction of E using five predictor variables, incident solar radiation (S), maximum temperature (T _max), minimum temperature (T _min), atmospheric vapor pressure (VP) and precipitation (P). The RVM model is based on the Bayesian formulation of a linear model with appropriate prior that results in sparse representations. The ELM model is computationally efficient algorithm based on Single Layer Feedforward Neural Network with hidden neurons that randomly choose input weights and the MARS model is built on flexible regression algorithm that generally divides solution space into intervals of predictor variables and fits splines (basis functions) to each interval. By utilizing random sampling process, the predictor data were partitioned into the training phase (70 % of data) and testing phase (remainder 30 %). The equations for the prediction of monthly E were formulated. The RVM model was devised using the radial basis function, while the ELM model comprised of 5 inputs and 10 hidden neurons and used the radial basis activation function, and the MARS model utilized 15 basis functions. The decomposition of variance among the predictor dataset of the MARS model yielded the largest magnitude of the Generalized Cross Validation statistic (≈0.03) when the T _max was used as an input, followed by the relatively lower value (≈0.028, 0.019) for inputs defined by the S and VP. This confirmed that the prediction of E utilized the largest contributions of the predictive features from the T _max, verified emphatically by sensitivity analysis test. The model performance statistics yielded correlation coefficients of 0.979 (RVM), 0.977 (ELM) and 0.974 (MARS), Root-Mean-Square-Errors of 9.306, 9.714 and 10.457 and Mean-Absolute-Error of 0.034, 0.035 and 0.038. Despite the small differences in the overall prediction skill, the RVM model appeared to be more accurate in prediction of E. It is therefore advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.     

Spatial modelling of reference evapotranspiration using adjusted Blaney-Criddle equation in an arid environment

Abstract

The Blaney-Criddle (BC) temperature-based equation is used in areas where the complete weather data to estimate reference evapotranspiration (ET₀) by the Penman-Monteith FAO-56 (PMF-56) standard model is complex. In this study, the BC equation was first tested and calibrated against the ET₀ values computed by the PMF-56 method using data from 17 weather stations in arid regions of Iran. Then, geographical information systems (GIS)-based spatially-distributed maps of ET₀ were prepared by means of geographic/topographic factors derived from a digital elevation model (DEM) for all months, separately. The results indicate that the original BC equation overestimated PMF-56 ET₀ by 4% at the study sites. The BC equation produced closer ET₀ estimates to the PMF-56 method after it was calibrated. The error rate of <3% for the spatial modelling approach suggests that the developed ET₀ maps are reliable.

Editor D. Koutsoyiannis; Associate editor D. Yang

Citation Tabari, H., Hosseinzadeh Talaee, P., and Shifteh Some'e, B., 2013. Spatial modelling of reference evapotranspiration using adjusted Blaney-Criddle equation in an arid environment. Hydrological Sciences Journal, 58 (2), 408–420.     

M5 model tree based modelling of reference evapotranspiration

This paper investigates the potential of M5 model tree based regression approach to model daily reference evapotranspiration using climatic data of Davis station maintained by California irrigation Management Information System (CIMIS). Four inputs including solar radiation, average air temperature, average relative humidity, and average wind speed whereas reference evapotranspiration calculated using a relation provided by the CIMIS was used as output. To compare the performance of M5 model tree in predicting the reference evapotranspiration, FAO–56 Penman–Monteith equation and calibrated Hargreaves–Samani relation was used. A comparison of results suggests that M5 model tree approach works well in comparison to both FAO–56 and calibrated Hargreaves–Samani relations. To judge the generalization capability of M5 model tree approach, model created by using the Davis data set was tested with the datasets of four different sites. Results from this part of the study suggest that M5 model tree could successfully be employed in modeling the reference evapotranspiration. Further, sensitivity analysis with M5 model tree approach suggests the suitability of solar radiation, average air temperature, average relative humidity, and average wind speed as input parameters to model the reference evapotranspiration Copyright © 2009 John Wiley & Sons, Ltd.     

Trend analysis of reference evapotranspiration in Northwest China: The roles of changing wind speed and surface air temperature

Reference evapotranspiration (ET₀) is an important element in the water cycle that integrates atmospheric demands and surface conditions, and analysis of changes in ET₀ is of great significance for understanding climate change and its impacts on hydrology. As ET₀ is an integrated effect of climate variables, increases in air temperature should lead to increases in ET₀. However, this effect could be offset by decreases in vapor pressure deficit, wind speed, and solar radiation which lead to the decrease in ET₀. In this study, trends in the Penman–Monteith ET₀ at 80 meteorological stations during 1960–2010 in the driest region of China (Northwest China) were examined. The results show that there was a change point for ET₀ series around the year 1993 based on the Pettitt's test. For the region average, ET₀ decreased from 1960 to 1993 by ?2.34 mm yr^?2, while ET₀ began to increase since 1994 by 4.80 mm yr^?2. A differential equation method based on the Food and Agriculture Organization Penman–Monteith formula was used to attribute the change in ET₀. The attribution results show that the significant decrease in wind speed dominated the change in ET₀, which offset the effect of increasing air temperature and led to the decrease in ET₀ from 1960 to 1993. However, wind speed began to increase, and the amplitude of increase in air temperature also rose significantly since the mid‐1990s. Increases in air temperature and wind speed together reversed the trend in ET₀ and led to the increase in ET₀ since 1994. Copyright © 2012 John Wiley & Sons, Ltd.     

Variation of reference evapotranspiration and its contributing climatic factors in the Poyang Lake catchment,China

By using linear regression (parametric), Mann–Kendall (nonparametric) and attribution analysis methods, this study systematically analysed the changing properties of reference evapotranspiration (ETr) calculated using the Penman–Monteith method over the Poyang Lake catchment during 1960–2008 and investigated the contribution of major climatic variables to ETr changes and their temporal evolution. Generally, a significant decreasing trend of annual ETr is found in the catchment. The decrease of annual ETr in the Poyang Lake basin is mostly affected by the decline of summer ETr. Over the study period, climatic variables, i.e. sunshine duration (SD), relative humidity (RH), wind speed (WS) and vapour pressure all showed decreasing trends, whereas mean daily temperature (DT) increased significantly. Multivariate regression analysis indicated that SD is the most sensitive climatic variable to the variability of ETr on annual basis, followed by RH, WS and DT, whereas the effect of vapour pressure is obscure. Although recent warming trend and decrease of relative humidity over the catchment could have increased ETr, the combined effect of shortened SD and reduced WS negated the effect and caused significant decrease of ETr. Our investigation reveals that the relative contributions of climatic variables to ETr are temporally unstable and vary considerably with large fluctuation. In consideration of the changes of climatic variables over time, further analysis indicated that changes of mean annual ETr in 1970–2008 were primarily affected by SD followed by WS, RH and DT with reference to 1960s. However, WS became the predominant factor during the period 2000–2008 compared with reference period 1960s, and followed by SD. Copyright © 2013 John Wiley & Sons, Ltd.     

Assessment of sediment transport approaches for sand-bed rivers by means of machine learning

Abstract

The quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches; however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed.

Editor Z.W. Kundzewicz     

The potential of different ANN techniques in evapotranspiration modelling

The potential of three different artificial neural network (ANN) techniques, the multi‐layer perceptrons (MLPs), radial basis neural networks (RBNNs) and generalized regression neural networks (GRNNs), in modelling of reference evapotranspiration (ET₀) is investigated in this paper. Various daily climatic data, that is, solar radiation, air temperature, relative humidity and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, USA, are used as inputs to the ANN techniques so as to estimate ET₀ obtained using the FAO‐56 Penman–Monteith (PM) equation. In the first part of the study, a comparison is made between the estimates provided by the MLP, RBNN and GRNN and those of the following empirical models: The California Irrigation Management Information System (CIMIS) Penman (1985), Hargreaves (1985) and Ritchie (1990). In this part of the study, the empirical models are calibrated using the standard FAO‐56 PM ET₀ values. The estimates of the ANN techniques are also compared with those of the calibrated empirical models. Mean square errors, mean absolute errors and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. Based on the comparisons, it is found that the MLP and RBNN techniques could be employed successfully in modelling the ET₀ process. In the second part of the study, the potential of ANN techniques and the empirical methods in ET₀ estimation using nearby station data is investigated. Among the models, the calibrated Hargreaves model is found to perform better than the others. Copyright © 2007 John Wiley & Sons, Ltd.     

Sensitivity analysis of a Penman–Monteith type equation to estimate reference evapotranspiration in southern Spain

Sensitivity analysis is crucial in assessing the impact of climatic variables on reference evapotranspiration estimations. The sensitivity of the standardized ASCE–Penman–Monteith evapotranspiration equation for daily estimations to climatic variables has not yet been studied in Spain. Andalusia is located in southern Spain where almost 1 million ha are irrigated under quite different conditions; it has a high inter‐annual variability in rainfall. In this study, sensitivity analyses for this equation were carried out for temperature, relative humidity, solar radiation and wind speed data from 87 automatic weather stations, including coastal and inland locations, from 1999 to 2006. Topography and Mediterranean climate characterize the heterogeneous landscape and vegetation of this region. Simulated random and systematic errors have been added to meteorological data to obtain ET₀ deviations and sensitivity coefficients for different time periods. BIAS and SEE (standard error of estimate) have been used to evaluate the effect of both types of errors. The results showed a large degree of daily and seasonal variability, especially for temperature and relative humidity. In general, the effect on ET₀ values of introduced random errors was larger than that of systematic errors. ET₀ overestimations were produced using positive errors in temperature, solar radiation and wind speed data, while these errors in relative humidity resulted in ET₀ underestimations. The sensitivity of ET₀ to the same climatic variables showed significant differences among locations. The geographical distribution of sensitivity coefficients across this region was also studied. As an example, during spring months, ET₀ equation was more sensitive to temperature in stations located along the Guadalquivir Valley. Copyright © 2009 John Wiley & Sons, Ltd.     

Comparison of radial basis function networks and empirical equations for converting from pan evaporation to reference evapotranspiration

Evapotranspiration (ET) is one of the major processes in the hydrological cycle, and its reliable estimation is essential to water resources management. Numerous equations have been developed for estimating ET, most of which are complex and require numerous items of weather data. In many areas, the necessary data are lacking, and simpler techniques are required. Evaporation pans are used throughout the world because of the simplicity of technique, low cost, and ease of application. In this study, the radial basis function (RBF) network is applied for pan evaporation to evapotranspiration conversions. The adaptive pan‐based RBF network was trained using daily Policoro data from 15 May 1981 to 23 December 1983. The RBF network obtained, Christiansen, FAO‐24 pan, and FAO‐56 Penman–Monteith equations were verified in comparison with lysimeter measurements of grass evapotranspiration using daily Policoro data from 25 February to 18 December 1984. Based on summary statistics, the RBF network ranked first with the lowest RMSE value (0·433 mm day^?1). The RBF network obtained on the basis of the daily data from Policoro, Italy and pan‐based equations were further tested using mean monthly data collected in Novi Sad, Serbia, and Kimberly, Idaho, USA. The overall results favoured use of the RBF network for pan evaporation to evapotranspiration conversions. The use of the RBF network is very simple and does not require any knowledge of ANNs. Users require only code (RBF network), E_pan data and corresponding R_a data. Copyright © 2009 John Wiley & Sons, Ltd.     

A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

ABSTRACT

A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs – the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX) – were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.     

Estimation of urban subtropical bahiagrass (Paspalum notatum) evapotranspiration using crop coefficients and the eddy covariance method

Eddy covariance (EC) and micro‐meteorological data were collected from May 2010 to January 2013 from urban, non‐irrigated bahiagrass (Paspalum notatum) in subtropical south Florida. The objectives were to determine monthly crop coefficients (K_c) for non‐irrigated bahiagrass by using EC evapotranspiration (ET) data and the Food and Agriculture Organization 56 Penman–Monteith reference evapotranspiration equation; compare crop ET (ET_c) calculated with new K_c values to ET_c obtained using K_c values available in the literature; and compare results and methodologies for statistical differences. New K_c values ranged from 0.62 to 0.92 and were different from K_c values found in the scientific literature for bahiagrass. Resulting ET_c calculated using literature K_c values were significantly different from EC ET data, whereas ET_c using the new K_c values was not. Specifically, literature K_c values were temporally biased to miscalculate the timing of convergence between potential and actual ET, assuming that our new K_c values calculated with EC methods were most accurate. As a consequence, ET_c calculated using the literature K_c values was either too large or too small. However, one set of literature K_c values from a similar climate and water table depth were closer to our new K_c values, indicating that climate should be considered when selecting urban non‐irrigated K_c from the literature to estimate ET. Results also indicated that more than 1 year of EC ET data was needed when establishing monthly K_c values because of annual variability in factors controlling ET, such as water availability. The new K_c values reported herein could be used as an estimate for urban non‐irrigated bahiagrass within similar climates. Copyright © 2013 John Wiley & Sons, Ltd.     

Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers

Landslides are considered as one among many phenomena jeopardizing human beings as well as their constructions. To prevent this disastrous problem, researchers have used several approaches for landslide susceptibility modeling, for the purpose of preparing accurate maps marking landslide prone areas. Among the most frequently used approaches for landslide susceptibility mapping is the Artificial Neural Network (ANN) method. However, the effectiveness of ANN methods could be enhanced by using hybrid metaheuristic algorithms, which are scarcely applied in landslide mapping. In the current study, nine hybrid metaheuristic algorithms, genetic algorithm (GA)-ANN, evolutionary strategy (ES)-ANN, ant colony optimization (ACO)-ANN, particle swarm optimization (PSO)-ANN, biogeography based optimization (BBO)-ANN, gravitational search algorithm (GHA)-ANN, particle swarm optimization and gravitational search algorithm (PSOGSA)-ANN, grey wolves optimization (GWO)-ANN, and probability based incremental learning (PBIL)-ANN have been used to spatially predict landslide susceptibility in Algiers’ Sahel, Algeria. The modeling phase was done using a database of 78 landslides collected utilizing Google Earth images, field surveys, and six conditioning factors (lithology, elevation, slope, land cover, distance to stream, and distance to road). Initially, a gamma test was used to decrease the input variable numbers. Furthermore, the optimal inputs have been modeled by the mean of hybrid metaheuristic ANN techniques and their performance was assessed through seven statistical indicators. The comparative study proves the effectiveness of the co-evolutionary PSOGSA-ANN model, which yielded higher performance in predicting landslide susceptibility compared to the other models. Sensitivity analysis using the step-by-step technique was done afterward, which revealed that the distance to the stream is the most influential factor on landslide susceptibility, followed by the slope factor which ranked second. Lithology and the distance to road have demonstrated a moderate effect on landslide susceptibility. Based on these findings, an accurate map has been designed to help land-use managers and decision-makers to mitigate landslide hazards.