Assessment of in situ permeability with emphasis on packer testing in Kuwait

This paper discusses in situ permeability testing performed at two sites in bore holes drilled in an extensive hydrogeological exploration program in Kuwait. The testing program, comprising packer, falling and rising head tests, was carried out in calcareous and variedly textured cemented sand formed during post-depositional diagenetic processes with the colloquial name ‘Gatch’. The program was undertaken to assist in the design and construction of a pilot drainage scheme aimed at lowering of the groundwater table in Kuwait City and suburbs.

The results indicated that ‘Gatch’ is an almost impermeable soil. However, the hydraulic conductivity of the subsoils at the investigated sites is significantly influenced by the distribution and volume of macropores. These permeable zones act as preferential pathways for the movement of water bypassing the fine matrix and, thereby, improve markedly the overall drainage characteristics of the sites.

Various in situ test methods, including packer, compare results satisfactorily, to give the sites a variable hydraulic conductivity rating that straddles between very low and medium. The packer device, by faring well against other routine in situ tests, shows promises for use as a tool in the assessment of drainage parameters of cemented soils. Laboratory tests, performed in parallel, proved inadequate to characterize the permeability properties of the subsoils in Kuwait.     

Practical seismic resilience evaluation and crisis management planning through GIS-based vulnerability assessment of buildings

The objective of this paper is to demonstrate how assessment of seismic vulnerability can be effective in protection against earthquakes.Findings are reported from a case study in a densely populated urban area near an active fault,utilizing practical methods and exact engineering data.Vulnerability factors were determined due to technical considerations,and a field campaign was performed to collect the required data.Multi-criteria decision making was carried out by means of an analytical hierarchy process including a fuzzy standardization.Earthquake scenarios were applied through an implicit vulnerability model.GIS was utilized and the results were analyzed by classifying the state of vulnerability in levels as very low,low,moderate,high,and very high.Seismic resilience was evaluated as vulnerabilities below the moderate state,being about 40% in an intensity of 6 Mercalli and less than 10% in 10 Mercalli.It is concluded that seismic resilience in the area studied is not acceptable,the area is vulnerable in the expected scenarios,and due to the high seismicity of the region,proper crisis management planning is required in parallel with attempts toward retrofitting.In this regard,an emergency map was developed with reference to the assessed vulnerabilities.     

Suspended sediment in the Ganges and Brahmaputra Rivers in Bangladesh: observation from TM and AVHRR data

Remote sensing was used to understand the seasonal and spatial variation of suspended sediment in the Ganges and Brahmaputra Rivers in Bangladesh for two different discharge periods. Suspended sediment concentration (SSC) in these rivers was estimated from the reflectance of Landsat TM band 3. During the high discharge period, SSC in the Ganges is higher than that in the Brahmaputra. But in the low discharge period, this is reversed. Both erosional and depositional processes are active on their flood plains. Significant fluctuations in SSC and in suspended sediment load were observed along their courses because of river bank erosion and deposition and/or scouring and aggradation of river beds. Owing to scouring and turbulence, SSC increases markedly at the confluence of these rivers. Reflectance of AVHRR band 1 data was also analysed to study the distribution of suspended sediment along other reaches of these rivers. Like SSC, reflectance at the confluence zone increases compared with that in the Brahmaputra. However, this increase is not marked compared with the Ganges. The influence of their tributaries on the suspended sediment load could be inferred from the pattern of reflectance. Remote sensing data used in this study was corrected for atmospheric effects. Copyright © 2001 John Wiley & Sons, Ltd.     

Precipitation analysis over southwest Iran: trends and projections

Analysis of trends and projection of precipitation are of significance for the future development and management of water resource in southwest Iran. This research has been divided into two parts. The first part consists of an analysis of the precipitation over 50 stations in the study region for the period 1950–2007. The trends in this parameter were detected by linear regression and significance was tested by t test. Mann–Kendall rank test was also employed to confirm the results. The second part of the research involved future projection of precipitation based on four models. The models used were Centre National de Recherches Meteorologiques (CNRM), European Center Hamburg Model (ECHAM), Model for Interdisciplinary Research on Climate (MIROCH) and United Kingdom Meteorological Office (UKMOC). Precipitation projections were done under B1 and A1B emissions scenarios. The results of precipitation series indicated that most stations showed insignificant trend in annual and seasonal series. The highest numbers of stations with significant trends occurred in winter while no significant trends were detected by statistical tests in summer precipitation. No decreasing significant trends were detected by statistical tests in annual and seasonal precipitation series. The result of projections showed that precipitation may decrease according to majority of the models under both scenarios but the decrease may not be large, except according to MIROCH model. Autumn precipitation may increase with higher rates than other seasons at the end of this century.     

Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables

ABSTRACT

The potential of different models – deep echo state network (DeepESN), extreme learning machine (ELM), extra tree (ET), and regression tree (RT) – in estimating dew point temperature by using meteorological variables is investigated. The variables consist of daily records of average air temperature, atmospheric pressure, relative humidity, wind speed, solar radiation, and dew point temperature (T_dew) from Seoul and Incheon stations, Republic of Korea. Evaluation of the model performance shows that the models with five and three-input variables yielded better accuracy than the other models in these two stations, respectively. In terms of root-mean-square error, there was significant increase in accuracy when using the DeepESN model compared to the ELM (18%), ET (58%), and RT (64%) models at Seoul station and the ELM (12%), ET (23%), and RT (49%) models at Incheon. The results show that the proposed DeepESN model performed better than the other models in forecasting T_dew values.     

Evaluating the efficacy of SVMs,BNs, ANNs and ANFIS in wave height prediction

Wave Height (WH) is one of the most important factors in design and operation of maritime projects. Different methods such as semi-empirical, numerical and soft computing-based approaches have been developed for WH forecasting. The soft computing-based methods have the ability to approximate nonlinear wind–wave and wave–wave interactions without a prior knowledge about them. In the present study, several soft computing-based models, namely Support Vector Machines (SVMs), Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for mapping wind data to wave height. The data set used for training and testing the simulation models comprises the WH and wind data gathered by National Data Buoy Center (NDBC) in Lake Superior, USA. Several statistical indices are used to evaluate the efficacy of the aforementioned methods. The results show that the ANN, ANFIS and SVM can provide acceptable predictions for wave heights, while the BNs results are unreliable.