Impact of flash flood recharge on groundwater quality and its suitability in the Wadi Baysh Basin,Western Saudi Arabia: an integrated approach

An integrated approach was used to evaluate the impact of flash flood recharge on groundwater quality and its suitability for drinking, irrigation, livestock and poultry uses in the Wadi Baysh Basin, Western Saudi Arabia. Analyses of 182 groundwater samples, collected from the study area before and after a flash flood (FF) event, show that the average concentrations of TDS, Mg, Na, Cl, NO₃ and EC decreased significantly after the event. The major water types (mixed CaMgCl, NaCl and CaCl) indicate that the infiltration of surface water from FF recharge has a great influence on groundwater chemistry. Drinking water suitability maps, created using WHO standards, indicate that wells located in the upstream region are suitable for drinking despite their high TDS and total hardness (TH) values. Groundwater in the coastal region is unsuitable due to its high salinity, high TH and high concentrations of major ions. The suitability of groundwater for irrigational use was assessed using salinity, sodium adsorption ratio, bicarbonate hazard, residual sodium carbonate, Kelly’s ratio, magnesium hazard, sodium percentage and permeability index values, which indicated that groundwater in the study region is suitable for most soils and crops. After FF, groundwater quality is improved by dilution, especially in the downstream region. USSL classification shows that the majority of the water samples are in the C3S1, C4S2, and C3S2 classes and are therefore suitable for the irrigation of salt-tolerant crops. Irrigational suitability maps suggest that wells in the upstream region are suitable for irrigation, whereas wells located near to the coast are unfit for irrigation. This study implies that construction of check dams in the dry valleys (wadies) may improve the groundwater quality in the area.     

Precipitation pattern modeling using cross-station perception: regional investigation

Establishing robust models for predicting precipitation processes can yield a significant aspect for many applications in water resource engineering and environmental prospective. In particular, understanding precipitation phenomena is crucial for managing the effects of flooding in watersheds. In this research, a regional precipitation pattern modeling was undertaken using three intelligent predictive models incorporating artificial neural network (ANN), support vector machine (SVM) and random forest (RF) methods. The modeling was carried out using monthly time scale precipitation information in a semi-arid environment located in Iraq. Twenty weather stations covering the entire region were used to construct the predictive models. At the initial stage, the region was divided into three climatic districts based on documented research. Initially, modeling was carried out for each district using historical information from regionally distributed meteorological stations for calibration. Subsequently, cross-station modeling was undertaken for each district using precipitation data from other districts. The study demonstrated that cross-station modeling was an effective means of predicting the spatial distribution of precipitation in watersheds with limited meteorological data.     

Spatial distribution of regionalized variables on reservoirs and groundwater resources based on geostatistical analysis using GIS: case of Rmel-Oulad Ogbane aquifers (Larache,NW Morocco)

In many countries of the world, groundwater is the main source of water in arid and semiarid regions. The scarcity of water is one of the main issues in Morocco. The coastal aquifer system of Rmel-Oulad Ogbane is recognized as one of the most important aquifers in Morocco and is very well known for their role in industrial, economic, and social development. However, this role is confronted to climate change impacts and heavy abstraction rates leading to a major decline in the groundwater levels and may eventually cause a deficit water balance of the aquifer as well as a degradation of the freshwater quality by seawater intrusion. The objective of this research is to identify and evaluate the distribution and spatial changes of regionalized variables on reservoirs and groundwater resources using geostatistical analysis in Geographic Information System (GIS) software. The prediction of these variables was performed using an interpolation method: ordinary kriging in a GIS. The normality test and trend analysis were applied to each variable to select the appropriate semivariogram model (SVM) and check the results using cross-validation (CV). Hence, several kriged maps of reservoirs and water resources have been produced to be exploited by the decision maker. The studied variables related to reservoirs and hydrodynamic data have a strong spatial dependence, which show correlations in specific direction, while the hydrochemical data are mainly related to groundwater mechanisms, such as advective-diffusive transport, without any autocorrelation between data.     

Sensitivity analysis of the seismic demands of RC moment resisting frames to different aspects of ground motions

A weight vector representing the relative importance of various characteristics of ground motions (GMs) and a conditioning intensity measure (IM) are required to be able to use the generalized conditional IM framework for the purpose of GM selection. An inappropriate weight vector may result in the biased distributions of some important characteristics of GMs and, consequently, the bias in the structural responses. This article aims to provide the analyst with the understanding of which properties of GMs are important in capturing the accurate structural responses, to specifically assign a suitable weight to them and to select an appropriate conditioning IM as well. To this end, 4 reinforced concrete buildings, located at the site in which the seismic hazard is dominated by shallow crustal earthquakes, are considered. The findings reveal that the appropriate weight vectors depend on the characteristics of the employed structural systems. In addition, the role played by each IM in capturing the true structural responses changes over different earthquake intensity levels implying that different weight vectors are required over different earthquake levels. Furthermore, this study shows that, even in case of shorter‐duration GMs from shallow events, GM duration should be incorporated in GM selection as it has effects on the peak‐based structural responses in the earthquake levels beyond the level of 2%‐in‐50‐years. Specifically, the findings reveal that in case of shallow events, unlike large magnitude earthquakes, the shorter the duration of GM the more rapid release of energy and, consequently, the larger the peak‐based structural responses.     

Sensitivity analysis of parameters influencing the ice–seabed interaction in sand by using extreme learning machine

Ice gouging problem is a significant challenge threatening the integrity of subsea pipelines in the Arctic (e.g., Beaufort Sea) and even non-Arctic (e.g., Caspian Sea) offshore regions. Determining the seabed response to ice scour through the subgouge soil deformations and the keel reaction forces are important aspects for a safe and cost-effective design. In this study, the subgouge soil deformations and the keel reaction forces were simulated by the extreme learning machine (ELM) for the first time. Nine ELM models (ELM 1–ELM 9) were developed using the key parameters governing the ice–seabed interaction. The number of neurons in the hidden layer was optimized and the best activation function for the ELM network was identified. The premium ELM model, resulting in the lowest level of inaccuracy and complexity and the highest level of correlation with experimental values was identified by performing a sensitivity analysis. The gouge depth ratio and the shear strength of the seabed soil were found to be the most influential input parameters affecting the subgouge soil deformations and the keel reaction forces. A set of the ELM-based equations were proposed to approximate the ice gouging parameters. The uncertainty analysis showed that the premium ELM model slightly underestimated the subgouge soil deformation.

     

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T _mean and R _H variables.     

New alternatives for reference evapotranspiration estimation in West Africa using limited weather data and ancillary data supply strategies.

Accurate estimation of reference evapotranspiration (ET ₀ ) is essential for the computation of crop water requirements, irrigation scheduling, and water resources management. In this context, having a battery of alternative local calibrated ET ₀ estimation methods is of great interest for any irrigation advisory service. The development of irrigation advisory services will be a major breakthrough for West African agriculture. In the case of many West African countries, the high number of meteorological inputs required by the Penman-Monteith equation has been indicated as constraining. The present paper investigates for the first time in Ghana, the estimation ability of artificial intelligence-based models (Artificial Neural Networks (ANNs) and Gene Expression Programing (GEPs)), and ancillary/external approaches for modeling reference evapotranspiration (ET ₀ ) using limited weather data. According to the results of this study, GEPs have emerged as a very interesting alternative for ET ₀ estimation at all the locations of Ghana which have been evaluated in this study under different scenarios of meteorological data availability. The adoption of ancillary/external approaches has been also successful, moreover in the southern locations. The interesting results obtained in this study using GEPs and some ancillary approaches could be a reference for future studies about ET ₀ estimation in West Africa.     

利用支持向量分类(SVC)估算断层深度和特征选择(英文)

地下断层深度的估算是重力解释难题之一,我们试利用支持向量分类(SVC)法进行计算。使用正演和非线性反演技术,通过相关误错使检测地下断层深度成为可能。但必要有一个深度初始猜测值,而且这猜测值通常不是由重力资料得。本文我们介绍以SVC作为利用重力数据估算断层深度的一种手段。在这项研究中,我们假设一种地下断层深度可归为一种类型,SVC作为一个分类算法。为了有效地利用此SVC算法,我们基于一个正确的特征选择算法去选择正确的深度特征。本次研究中我们建立了一套基于不同深度地下断层的合成重力剖面训练集,用以训练用于计算实际的地下断层深度的SVC代码。然后用其它合成重力剖面训练集测试我们训练的SVC代码,同时也用实际资料验证了我们的训练SVC代码。     

On the number of required response history analyses

This study investigates the number of real ground motions (GMs) required to accurately capture the structural responses when using code-, intensity-, and risk-based seismic assessment frameworks. A key consideration is the use of as-recorded hazard consistent GMs, which appropriately characterize the seismic hazard of the site, for performing response history analysis. A comparison of the structural responses of 3 steel moment resisting buildings using different sets of GMs is made with results obtained using a large set of GMs. The empirical results demonstrate that, in most cases, a risk-based assessment needs a larger number of response history analyses than an intensity-based assessment method to accurately capture the seismic demand on the structural systems. Also, given the same number of GMs, the structural responses from a risk-based assessment are more reliable and stable than those for an intensity-based method. Moreover, the results help to quantify the uncertainty in the structural responses due to using different sample sets of GMs given the applied number of GMs. In addition, the results indicate that a significant bias in structural responses is likely when following the recommendations from several seismic design codes, which suggest utilizing the maximum structural responses if 3 GM pairs are used for response history analysis. A detailed statistical analyses show that the application of 7 GM pairs are sufficient to accurately and reliably estimate structural responses.