For safe design of culverts, the estimation of scour depth downstream of the culvert outlet is one of the most remarkable considerations. Poor design of culverts inevitably leads to destruction and increases expenditures on management and rehabilitation. The objective of this study is to investigate the performance of numerical models in predicting scour depth and its location downstream of box culverts in unsteady flow conditions. Culverts with unblocked and partially blocked outlets were used, and the predictions of two turbulence methods, namely renormalization group (RNG) and $k\text{-}\epsilon$ , were compared. Comprehensive experimental data of box culverts from the literature were used and the numerical models were performed in commercially available software. The downstream profile of scour development, maximum scour depth, and its location were obtained and compared with observed data. It was found that the numerical model was in good agreement with the experimental data. In both turbulence models, the scour depth increased with increase of discharge in the raising limb of the hydrograph, whereas for lower discharges in the falling limb of the hydrograph, the scour depth decreased.

A groundwater quality map serves as a deterrent mechanism that provides insight into likely environmental health predicaments. The objective of this study was to create map and evaluate the quality and changes in groundwater during the study period in Erbil, Iraq. Based on the 13 groundwater parameters, the water quality index (WQI) was calculated for 61 wells from 2015 to 2017 for wet and dry seasons. To generate WQI maps, two geostatistical analyst tools in Geographical Information Systems, including Kriging and Inverse Distance Weighted (IDW) were used. For determining the most suitable method, statistical indices were applied to the obtained data. The results showed that the Kriging method increases the prediction accuracy compared to the IDW method. The water quality in 2017 was decreased compared to the previous years and the WQI was increased from 1.64% to 11.47%. Untreated domestic and industrial wastewater causes groundwater pollution which was the main reason for the decrease in the water quality of Erbil city.

Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment and can help to lessen the negative effects of climate change by lowering the level of emissions of greenhouse gas. This study developed thirteen different artificial intelligence models, including multivariate adaptive regression splines (MARS), extreme learning machine (ELM), Kernel extreme learning machine (KELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), outlier robust extreme learning machine (ORELM), deep extreme learning machine (DELM), and their versions combined with variational mode decomposition (VMD) as integrated models (VMD-DELM, VMD-ORELM, VMD-OPELM, VMD-OSELM, VMD-KELM, and VMD-ELM), for solar radiation estimation in Kurdistan region, Iraq. The daily meteorological data from 2017 to 2018 were used to implement suggested artificial models at Darbandikhan and Dukan stations, Iraq. The input parameters included daily data for maximum temperature (MAXTEMP), minimum temperature (MINTEMP), maximum relative humidity (MAXRH), minimum relative humidity (MINRH), sunshine duration (SUNDUR), wind speed (WINSPD), evaporation (EVAP), and cloud cover (CLOUDCOV). The results show that the proposed VMD-DELM algorithm considerably enhanced the simulation accuracy of standalone models’ daily solar radiation prediction, with average improvement in terms of RMSE of 13.3%, 20.36%, 25.1%, 27.1%, 34.17%, 38.64%, and 48.25% for Darbandikhan station and 5.22%, 10.01%, 10.26%, 21.01%, 29.7%, 35.8%, and 40.33% for Dukan station, respectively. The outcomes of this study reveal that the VMD-DELM two-stage model performed superiorly to the other approaches in predicting daily solar radiation by considering climatic predictors at both stations.

Modelling at the basin scale offers crucial insights for policymakers as they make decisions regarding the optimal utilization of water resources. This study employed the MIKE HYDRO Basin model to analyse water demand and supply dynamics in the Parvati Basin of Rajasthan, India, for the period 2005–2020. The MIKE11 NAM model showcased strong alignment between simulated and observed runoff during both the calibration (NSE = 0.79, PBIAS = −2%, R² = 0.79, RMSE = 4.95, RSR = 0.5, and KGE = 0.84) and validation (NSE = 0.67, PBIAS = −12.4%, R² = 0.68, RMSE = 8.3, RSR = 0.62, and KGE = 0.67) phases. The MIKE HYDRO Basin model also exhibited excellent agreement between observed and simulated reservoir water levels, with R², NSE, RMSE, PBIAS, RSR, and KGE values of 0.86, 0.81, 3.87, −2.30%, 0.43, and 0.88, respectively. The MIKE HYDRO Basin model was employed to create six distinct scenarios, considering conveyance efficiency, irrigation method, and conjunctive water use, to assess irrigation demands and deficits within the basin. In the initial simulation, featuring a conveyance efficiency of 45%, flood irrigation, and no groundwater utilization, the average water demand and deficit throughout the study period were estimated as 43.15 MCM and 3.45 MCM, respectively, resulting in a sustainability index of 0.506. Enhancing conveyance efficiency to 75% under flood irrigation and 5% conjunctive use could elevate the sustainability index to 0.92. Transitioning to sprinkler irrigation and a lift irrigation system could raise the system’s sustainability index to 1. These developed models hold promise for real-time reservoir operation and irrigation planning across diverse climatic conditions and varying cropping patterns.