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•Modeling long term evapotranspiration of wheat based on limited meteorological data.•Future ETc will increase by 11.31 % and 1.38 % in Kafr Ash shaykh and Ad Daqahliyah while Ash ...Sharqiyah would decrease by 15.09 % compared to historical data.•Superior DNN generated high correlation between actual versus predicted ETc reached to 0.97 and 0.95 for calibration and validation.•Highly increase in future Tmax by 20.82 % will be in Ash Sharqiyah while Kafr ash Shaykh will slightly increase by 4.22 %.
Crop evapotranspiration (ETc) is one of the most basic components of the hydrologic cycle that is effective in irrigation system design and management, water resources planning and scheduling, and hydrologic water balance. Thus, precise estimation of ETc is valuable for various applications of agricultural water engineering, especially in developing countries such as Egypt, which has lack of meteorological data, high cost and time to calculate ETc, and lack of information on future ETc values to consider management scenarios and increase production potential. Also, due to the existence of different climates in Egypt, the estimate of ETc has become a challenge. To this end, the aim of this study was to estimate the ETc to eliminate the limitations mentioned, and analyze the long-term dynamics of ETc based on limited climate data and simple method. Three Egyptian governorates namely Ad Daqahliyah, Ash Sharqiyah, and Kafr ash Shaykh of Nile Delta, were selected as major wheat-producing sites. The required historical required climatic data were collected from open access data library while future data were from two extreme scenarios of the Representative Concentration Pathways (RCP) i.e., RCP 4.5, and RCP 8.5. The available dataset was divided into three parts: (i) calibration from 1970−2000, (ii) validation from 2000−2017, and (iii) prediction from 2022−2035. The deep neural network (DNN) was employed for incorporating historical data and predicting future ETc. For the evaluation of generated DNN models, the research finding indicates that the correlation coefficients between actual versus predicted monthly ETc were found to be 0.95, 0.96, and 0.97 for calibration period, and 0.94, 0.95 and 0.95 for validation at Ad Daqahliyah, Kafr ash Shaykh, and Ash Sharqiyah regions, respectively. For the simulation of future climatic data, maximum temperature (Tmax) will increased by 5.19 %, 4.22 %, and 20.82 %, minimum temperature (Tmin) will increased by 1.62 %, 36.44 %, and 27.80 %, and solar radiation (SR) will increased by 6.53 %, 18.74 %, and 28.83 % for the study locations, respectively. Moreover, the DNN model exposed that the Kafr ash Shaykh attain the highest values of ETc with an increase of 11.31 %, slightly increased of 1.38 % for Ad Daqahliyah, and decreased by 15.09 % for Ash Sharqiyah in comparison to the historical data. Thus, the proposed model of crop water-use prediction effectively estimated ETc of wheat and make an efficient decision. The developed models produced satisfactory results for water managers to save water and achieve the sustainability of agricultural water.
The worsening water scarcity has imposed a significant stress on food production in many parts of the world. This stress becomes more critical when countries seek self-sufficiency. A literature ...review shows that food self-sufficiency has not been assessed as the main factor in determining the optimal cultivation patterns. However, food self-sufficiency is one of the main policies of these countries and requires the most attention and concentration. Previous works have focused on the virtual water trade to meet regional food demand and to calculate trade flows. The potential of the trade network can be exploited to improve the cropping pattern to ensure food and water security. To this end, and based on the research gaps mentioned, this study develops a method to link intra-country trade networks, food security, and total water footprints (WFs) to improve food security. The method is applied in Iran, a water-scarce country. The study shows that 781 × 10
m
of water could be saved by creating a trade network. Results of the balanced trade network are input to a multi-objective optimization model to improve cropping patterns based on the objectives of achieving food security and preventing water crises. The method provides 400 management scenarios to improve cropping patterns considering 51 main crops in Iran. Results show a range of improvements in food security (19-45%) and a decrease in WFs (2-3%). The selected scenario for Iran would reduce the blue water footprint by 1207 × 10
m
, and reduce the cropland area by 19 × 10
ha. This methodology allows decision makers to develop policies that achieve food security under limited water resources in arid and semi-arid regions.
Simulation of groundwater quality is important for managing water resources and mitigating water shortages, especially in arid and semiarid areas. Geostatistical models have been used for spatial ...prediction and interpolation of groundwater parameters. Recently, hybrid intelligent models have been employed for the simulation of dynamic systems. In this study, hybrid intelligent models, based on a neuro-fuzzy system integrated with fuzzy c-means data clustering (FCM) and grid partition (GP) models as well as artificial neural networks integrated with particle swarm optimization algorithm, were used to predict the spatial distribution of chlorine (Cl), electrical conductivity (EC), and sodium absorption ratio (SAR) parameters of groundwater. Results of the hybrid models were compared with geostatistical methods, including kriging, inverse distance weighting (IDW), and radial basis function (RBF). The latitude and longitude values of observation wells and qualitative parameters in three states of maximum, average, and minimum were introduced as input and output to the models, respectively. To evaluate the models, the root mean squared error (RMSE), the mean absolute error (MAE), and CC statistical criteria were used. Results showed that in the hybrid models, NF-GP with the lowest RMSE and MAE and highest CC was the most suitable model for the prediction of water quality parameters. The RMSE, MAE, and CC values were 107.175 (mg/L), 79.804 (mg/L), and 0.924 in the average state for Cl; were 518.544 (μmho/cm), 444.152 (μmho/cm), and 0.882 for electrical conductivity; and were 1.596, 1.350, and 0.582 for sodium absorption ratio, respectively. Among the geostatistical models, the kriging was found more accurate. Using the coordinates of wells will eventually allow the NF-GP to be used for more sampling and replace the visual techniques that require more time, cost, and facilities.
•A newly developed hybrid intelligent model is proposed for ETo estimation.•The proposed model is covering all available climatic regimes of Iran.•Entropy was used to identify relationships between ...meteorological variables.•The ANN-GWO provided the most accurate results among the applied approaches.•This model solves the problem of shortage of weather information in each climate of Iran in the estimation of ETo.
Reference Evapotranspiration (ETo) is one of the key components of the hydrological cycle that is effective in water resources planning, irrigation and agricultural management and, other hydrological processes. Accurate estimation of ETo is valuable for various applications of water resource engineering, especially in developing countries such as Iran, which has no advanced meteorological stations and lacks facilities and information. Also, due to the existence of different climates in Iran, the estimate of ETo has become a challenge. To this end, the aim of this study is to estimate the ETo to eliminate the two limitations of the absence of a comprehensive model for all climates and the scarcity of meteorological information in Iran. The present study investigates the ability of the hybrid artificial neural network- Gray Wolf Optimization (ANN-GWO) model to estimate ETo for Iran. The accuracy of ANN-GWO was evaluated versus least square support vector regression (LS-SVR) and standalone ANN. The development of models is based on meteorological data of Iran’s 31 provinces consists of 5 different climates. Based on empirical equations and least inputs, seven different input scenarios were introduced and Penman-Monteith reference evapotranspiration was considered as the output of the models. Several statistical indicators including SI, MAE, U95, R2, Global Performance Indicator (GPI), and Taylor diagram were used to evaluate the performance of the models. The results showed that the GWO algorithm acted as an efficient tool in optimizing the structure of the ANN and the ANN-GWO model was more accurate than ANN and LS-SVR in all scenarios. ANN-GWO6 with inputs of wind speed, maximum and minimum temperatures, had the lowest error and decreased in terms of SI index by 42% (compared to ANN6) and 30% (compared to LS-SVR6). Furthermore, based on GPI, it is in the first place with a 99% reduction, compared to ANN6 and LS-SVR6. The hybrid approach used in this study can be developed as a trustful expert intelligent system for estimating ETo in Iran.
Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research investigated the applicability of ...Shannon’s entropy theory and correlation in obtaining the combination of the optimum inputs, and then the abstracted input variables were used to develop three novel intelligent hybrid models, namely, NF-GWO (neuro-fuzzy with grey wolf optimizer), NF-SC (subtractive clustering), and NF-FCM (fuzzy c-mean), for estimation of DO concentration. Seven different input combinations of water quality variables, including water temperature (TE), specific conductivity (SC), turbidity (Tu), and pH, were used to develop the prediction models at two stations in California. The performance of proposed models for DO estimation was assessed using statistical metrics and visual interpretation. The results revealed the better performance of NF-GWO for all input combinations than other models where its performance was improved by 24.2–66.2% and 14.9–31.2% in terms of CC (correlation coefficient) and WI (Willmott index) compared to standalone NF for different input combinations. Additionally, the MAE (mean absolute error) and RMSE (root mean absolute error) of the NF model were reduced using the NF-GWO model by 9.9–46.0% and 8.9–47.5%, respectively. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model for predicting DO concentration of the two stations. In contrast, NF-SC performed worst for most of the input combinations. The violin plot of NF-GWO-predicted DO was found most similar to the violin plot of observed data. The dissimilarity with the observed violin was found high for the NF-FCM model. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.
•A newly developed hybrid intelligent model is proposed for soil moisture prediction.•The proposed model is validated against well-established machine learning models.•Several related morphological ...variables are used to build the predictive models.•The designed predictive models are evaluated and assessed comprehensively.•The ANFIS-GWO is showed a robust and reliable model for soil moisture prediction.
Accurate estimation of soil moisture content is necessary for optimal management of water and soil resources. Soil moisture is an important variable in the hydrologic cycle, which plays an important role in the global water and energy balance due to its impact on hydrological, ecological, and meteorological processes. The purpose of the present study was to explore a newly developed hybrid intelligent model for simulating soil moisture content. A hybrid adaptive neuro fuzzy inference system (ANFIS) model-grey wolf optimization (GWO) algorithm was designed here and validated against the neural network (ANN), support vector regression (SVR) and standalone ANFIS models. The models input parameters were di-electric constant, soil bulk density, clay content and organic matter of 1155 soil samples. Various statistical indices were employed to evaluate the performances of the applied models. For a reliable ranking of models, the Global Performance Indicator (GPI) was utilized, which is a 5-agent index. The results evidenced the feasibility of the developed hybrid ANFIS-GWO model with superior simulation results. At the testing stage, the MAE and SI values for the ANFIS-GWO were 1.468% and 0.098, respectively, which indicated the superiority of the ANFIS-GWO compared to the ANFIS-Fuzzy C mean (MAE = 6.427%, SI = 0.354), and ANFIS-sub clustering (MAE = 2.137%, SI = 0.141) models. Based on the GPI, the ANFIS-GWO model was ranked as the best model, followed by the standalone ANFIS and SVR models, while the worst accuracy was attained through ANN model. The ANFIS-GWO model improved the simulation accuracy by 48% and 50%, respectively, compared to the standalone ANFIS and SVR models. In addition, based on the GPI, the ANFIS-GWO model presented an enhancement of about 77 percent compared to the ANN model. The high accuracy of the ANFIS-GWO model compared to the standalone ANFIS model represents the performance of the GWO algorithm for escaping local optima, which makes the ANFIS-GWO as a powerful tool for estimating soil moisture. Overall, the explored hybrid intelligent models demonstrated a reliable pedotransfer function of soil moisture where it can contribute to several geo-sciences engineering principles.
•Irrigation uniformity (CU) was modeled using data driven models.•5 data driven models were applied for modeling CU.•k-fold testing was adopted for assessing the models.•All applied models showed ...acceptable results in modeling CU.
The coefficient of uniformity (CU), an important parameter in design of irrigation systems, affects the quality and return of investment in irrigation projects significantly, and is a good indicator of water losses. In this paper, a single model was proposed to obtain the CU values in four sprinkler types of ZK30, ZM22, AMBO, and LUXOR. Average wind speed, coarseness index (large and small nozzle diameters), and sprinkler/lateral spacing were used as input parameters to obtain the CU values through employing the artificial neural networks (ANN), neuro-fuzzy grid partitioning (NF-GP), neuro-fuzzy sub-clustering (NF-SC), least square support vector machine (LS-SVM) and gene expression programming (GEP) techniques. The available data set consisted of 294 samples that were used to evaluate the proposed methodology. The applied techniques were assessed through the robust k-fold testing data assignment mode. Based on the results, all the applied models presented good capability in estimating CU. The obtained results revealed that the coarseness index (large nozzle diameter) had the lowest impact on modeling CU is sprinkler irrigation systems.
•Discharge of drip tape emitters was modeled using artificial intelligence (AI) models.•AI models were applied under simultaneous variations of temperature and pressure.•k-fold testing was adopted ...for assessing the models.•All applied models showed acceptable results in modeling discharge.
One of the effective factors to ensure the desirable operation of drip irrigation systems is the uniform emitter discharge, which is affected by operating pressure and temperature. Accurate estimation of this parameter is crucial for optimal irrigation system management and operation. In this research, the emitter outflow discharge was simulated through artificial intelligence (AI)-based approaches under a wide range of temperature (13−53 °C) and operating pressures (0–240 kPa) variations. The applied AI models included artificial neural networks (ANN), neuro-fuzzy sub-clustering (NF-SC), neuro-fuzzy c-Means clustering (NF-FCM), and least square support vector machine (LS-SVM). The input parameters matrix consisted of operating pressure, water temperature, discharge coefficient, pressure exponent and nominal discharge, while the ratio of measured discharge to nominal discharge (modified coefficient, M) was the output of the models. The applied models were assessed through the robust k-fold testing data scanning mode. The 5-agent Global Performance Indicator (GPI) was used for the final reliable ranking. The results showed that all the applied AI models with an average mean absolute error (MAE) of 8.8% had acceptable accuracy for estimating the modified M coefficient. According to the GPI, the LS-SVM model had the lowest error, followed by the NF-SC model with a slight difference.
•The effect of proper irrigation management in the solid-set systems was investigated.•Performance indices were evaluated in ten different local plots in 2007 and 2017.•The performance of the ...selected systems remained unchanged.•Selected systems were modified based on preliminary design considerations and re-evaluated in 2017.•In all systems CU, PELQ, and DP significantly improved.
Farmer's management is considered a key factor for efficient, cost-effective irrigation. In this study, the effect of proper irrigation management in the solid-set systems of Kurdistan province in Iran was investigated. Performance indices including Coefficient of Uniformity (CU), Potential Application Efficiency of the Low Quarter (PELQ), Wind Drift and Evaporation Loss (WDEL), Adequacy of Irrigation (ADirr), Deep Percolation Losses (DP), and Application Efficiency (AE) were measured in ten different local plots in 2007 and 2017. The performance of selected systems remained similar in 2007 and 2017. The management of the plots evaluated in 2017 was modified, taking into account the preliminary design considerations and applying changes such as irrigation scheduling and sprinkler type. These plots with improved management were re-evaluated. Results showed that CU and PELQ were improved by 29% and 82%, respectively, in all systems. In addition, deep percolation losses (DP) were reduced by more than 40%. Insufficient training for farmers and the use of unskilled labor are of the main reasons for sustained underperformance in the study area. Actions are required in farmers’ education and in public performance monitoring. Additionally, the results of this research have shown that management improvement measurements are urgently required for the satisfactory performance of the above-mentioned systems.
First of all, the authors thank the distinguished discusser for providing helpful comments on the original paper. But the following answers and explanations are essential.
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