Mapping of hard rock aquifer system and artificial recharge zonation through remote sensing and GIS approach in parts of Perambalur District of Tamil Nadu,India

Mapping of hard rock aquifer system and artificial recharge zonation were carried out in an area of 325 km^2 in parts of the Perambalur District,Tamil Nadu,India.This district has been declared as one of the over-exploited regions in Tamil Nadu by the Central Groundwater Board.To raise the groundwater level,suitable recharge zones were identified and artificial recharge structures are suggested using geomatics technology in the present study.To this end,various thematic maps concerning lithology,soil,geomorphology,land use,land cover,slope,lineament,lineament density,drainage,drainage density and groundwater depth level were prepared.Fissile hornblende gneiss(244 km^2)covered most of the study area followed by charnockites(68 km^2).Structural hills and rocky pediments characterize the major geomorphological features in the targeted area,and are followed by deep moderated pediments.The area is mostly used as crop and fallow land,followed by scrub land and deciduous forest.In the study area,the slopes are predominantly very gentle(142 km^2)and nearly level(66 km^2)ones.Besides,Groundwater level data of 58 wells have been generated,in which the minimum and maximum depth were 3 and 28 m respectively.Integration under the GIS environment has been carried out using all the thematic layers to identify the groundwater prospect zone through the introduction of weight and rank methods.Integrated output performances were classified into very poor,poor,moderate,good and excellent categories.All these classes were further divided into two groups as suitable and non-suitable area for the selection of recharge sites.Hard rock fractures were mapped as lineaments from satellite images,and besides that,rose diagram was also generated to find out the trend of the fracture.Furthermore,fracture data of 146 numbers have been collected using Brunton compass to generate rose diagram and were correlated with the rose diagram derived from lineaments.The present study significantly brought up a few areas such as Ammapalayam,Melapuliyur,Senjeri and around Siruvachur for artificial recharge.     

Mapping of hard rock aquifer system and artificial recharge zonation through remote sensing and GIS approach in parts of Perambalur District of Tamil Nadu,India

Mapping of hard rock aquifer system and artificial recharge zonation were carried out in an area of 325 km2 in parts of the Perambalur District, Tamil Nadu, India. This district has been declared as one of the over-exploited regions in Tamil Nadu by the Central Groundwater Board. To raise the groundwater level, suitable recharge zones were identified and artificial recharge structures are suggested using geomatics technology in the present study. To this end, various thematic maps concerning lithology, soil, geomorphology, land use, land cover, slope, lineament, lineament density, drainage, drainage density and groundwater depth level were prepared. Fissile hornblende gneiss (244 km2) covered most of the study area followed by charnockites (68 km2). Structural hills and rocky pediments characterize the major geomorphological features in the targeted area, and are followed by deep moderated pediments. The area is mostly used as crop and fallow land, followed by scrub land and deciduous forest. In the study area, the slopes are predominantly very gentle (142 km2) and nearly level (66 km2) ones. Besides, Groundwater level data of 58 wells have been generated, in which the minimum and maximum depth were 3 and 28 m respectively. Integration under the GIS environment has been carried out using all the thematic layers to identify the groundwater prospect zone through the introduction of weight and rank methods. Integrated output performances were classified into very poor, poor, moderate, good and excellent categories. All these classes were further divided into two groups as suitable and non-suitable area for the selection of recharge sites. Hard rock fractures were mapped as lineaments from satellite images, and besides that, rose diagram was also generated to find out the trend of the fracture. Furthermore, fracture data of 146 numbers have been collected using Brunton compass to generate rose diagram and were correlated with the rose diagram derived from lineaments. The present study significantly brought up a few areas such as Ammapalayam, Melapuliyur, Senjeri and around Siruvachur for artificial recharge.     

Deciphering potential groundwater zone in hard rock through the application of GIS

Remote Sensing and Geographic Information System has become one of the leading tools in the field of hydrogeological science, which helps in assessing, monitoring and conserving groundwater resources. It allows manipulation and analysis of individual layer of spatial data. It is used for analysing and modelling the interrelationship between the layers. This paper mainly deals with the integrated approach of Remote Sensing and geographical information system (GIS) to delineate groundwater potential zones in hard rock terrain. The remotely sensed data at the scale of 1:50,000 and topographical information from available maps, have been used for the preparation of ground water prospective map by integrating geology, geomorphology, slope, drainage-density and lineaments map of the study area. Further, the data on yield of aquifer, as observed from existing bore wells in the area, has been used to validate the groundwater potential map. The final result depicts the favourable prospective zones in the study area and can be helpful in better planning and management of groundwater resources especially in hard rock terrains.     

基于人工神经网络模型的岩石特性预测

近年来,软计算技术被用作替代的统计工具。如人工神经网络（ANN）被用于开发预测模型来估计所需的参数。在本研究中,通过利用冲击钻进过程中的一些钻进参数（气压、推力、钻头直径、穿透率）和所产生的声级,建立了预测岩石性质的神经网络模型。在实验室中所产生的数据,用于开发预测岩石特性（如单轴抗压强度、耐磨性、抗拉强度和施密特回弹数）的神经网络模型,并使用各种预测性能指标对所建模型进行检验,结果表明人工神经网络模型适用于岩石性质的预测。     

Integrated studies for characterization of lineaments used to locate groundwater potential zones in a hard rock region of Karnataka, India

An integrated study was carried out to investigate the subsurface geological conditions in a hard rock environment, with the aim of identifying zones with groundwater resource potential. The study, in Bairasagara watershed, Karnataka, India, considered geomorphology, water level, resistivity imaging, self potential, total magnetic field and susceptibility. The signatures due to lineaments have been clearly identified and their role in groundwater movement has been documented. Synthetic simulation methods were used to model the electrical response of the lineament using finite differential modeling scheme. The inverted image of the field data is compared with the synthetic image and iteration were performed on the initial model until a best match was obtained resulting on the generation of the calibrated resistivity image of the subsurface. Resistivity imaging revealed that the dykes are weathered/fractured to a depth of 6–8 m and are compact at deeper levels, and that they behave as barriers to groundwater movement, yet facilitate a good groundwater potential zone on the upgradient side. The results of magnetic surveys were utilized in differentiating granites and dolerite dykes with an insignificant resistivity contrast. Geomorphological expression alone cannot reveal the groundwater potential associated with a lineament. However, characterizing the nature of the feature at depth with integrated geophysical methods provides essential information for assessing that potential.An erratum to this article can be found at     

Integrated studies for characterization of lineaments used to locate groundwater potential zones in a hard rock region of Karnataka, India

An integrated study was carried out to investigate the subsurface geological conditions in a hard rock environment, with the aim of identifying zones with groundwater resource potential. The study, in Bairasagara watershed, Karnataka, India, considered geomorphology, water level, resistivity imaging, self potential, total magnetic field and susceptibility. The signatures due to lineaments have been clearly identified and their role in groundwater movement has been documented. Synthetic simulation methods were used to model the electrical response of the lineament using finite differential modeling scheme. The inverted image of the field data is compared with the synthetic image and iteration were performed on the initial model until a best match was obtained resulting on the generation of the calibrated resistivity image of the subsurface. Resistivity imaging revealed that the dykes are weathered/fractured to a depth of 6–8 m and are compact at deeper levels, and that they behave as barriers to groundwater movement, yet facilitate a good groundwater potential zone on the upgradient side. The results of magnetic surveys were utilized in differentiating granites and dolerite dykes with an insignificant resistivity contrast. Geomorphological expression alone cannot reveal the groundwater potential associated with a lineament. However, characterizing the nature of the feature at depth with integrated geophysical methods provides essential information for assessing that potential.The online version of the original article can be found at     

Artificial neural network model for prediction of rock properties from sound level produced during drilling

In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill bit diameter, drill bit speed, penetration rate of the drill bit and equivalent sound level (L_eq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (V_p), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) of intact rock for output, was established. The constructed models were checked using various prediction performance indices. Goodness of the fit measures revealed that recommended ANN model fitted the data as accurately as experimental results, indicating the usefulness of artificial neural networks in predicting rock properties.     

Application of visual,statistical and artificial neural network methods in the differentiation of water from the exploited aquifers in Kuwait

The ability of artificial neural network to differentiate water samples from the two aquifers of Kuwait on the basis of their major ion chemistry has been demonstrated. The major ion concentration distribution in the groundwater of the Kuwait Group and the Dammam Formation aquifers of Kuwait appears very similar. Cross-plots, supported by the discriminant function analysis of the data, however, suggest that there are some subtle differences in the overall composition of the water from the two aquifers that make it possible to differentiate the water from the two aquifers in almost 80% of the cases. An artificial neural network improved the differentiation capability to 90% of the cases. It is also possible to estimate the fraction of Kuwait Group water in the flow stream of dually completed wells with the help of an artificial neural network developed for this purpose. Electronic Publication     

Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network

Al-Mansourieh zone is a part of Al-Khalis City within the province of Diyala and located in the Diyala River Basin in eastern Iraq with a total area about 830 km2.Groundwater is the main water source for agriculture in this zone.Random well drilling without geological and hydraulic information has led the most of these wells to dry up quickly.Therefore,it is necessary to estimate the levels of groundwater in wells through observed data.In this study,Alyuda NeroIntelligance 2.1 software was applied to predict the groundwater levels in 244 wells using sets of measured data.These data included the coordinates of wells(x,y),elevations,well depth,discharge and groundwater levels.Three ANN structures(5-3-3-1,5-10-10-1 and 5-11-11-1)were used to predict the groundwater levels and to acquire the best matching between the measured and ANN predicted values.The coefficient of correlation,coefficient determination(R2)and sum-square error(SSE)were used to evaluate the performance of the ANN models.According to the ANN results,the model with the three structures has a good predictability and proves more effective for determining groundwater level in wells.The best predictor was achieved in the structure 5-3-3-1,with R2 about 0.92,0.89,0.84 and 0.91 in training,validation,testing and all processes respectively.The minimum average error in the best predictor is achieved in validation and testing processes at about 0.130 and 0.171 respectively.On the other hand,the results indicated that the model has the potential to determine the appropriate places for drilling the wells to obtain the highest level of groundwater.     

NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar,Bangladesh

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R~2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.     

隧道围岩破坏模式的进化神经网络识别

隧道围岩破坏受很多因素的影响,其破坏模式的识别是一个复杂的非线性系统辨识问题,采用一般方法很难得到好的解答。基于作者提出的免疫进化规划,并把它同神经网络(NN)相结合,提出了一种全新的结构及权值同时进化的进化神经网络(ENN)模型,用于围岩破坏模式的识别研究,用一个试验算例证明了进化神经网络具有解决此问题的良好性能。     

Undrained lateral load capacity of piles in clay using artificial neural network

This paper describes the application of the artificial neural network model to predict the lateral load capacity of piles in clay. Three criteria were selected to compare the ANN model with the available empirical models: the best fit line for predicted lateral load capacity (Q_p) and measured lateral load capacity (Q_m), the mean and standard deviation of the ratio Q_p/Q_m and the cumulative probability for Q_p/Q_m. Different sensitivity analysis to identify the most important input parameters is discussed. A neural interpretation diagram is presented showing the effects of input parameters. A model equation is presented based on neural network parameters.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

Groundwater level monitoring-importance global groundwater monitoring network

The paper illustrates the concept, methodology, essential components and importance of groundwater level monitoring in terms of various aquifers such as multiple aquifer, karst aquifer and other aquifers. The groundwater resources in Mekong countries including Cambodia, Laos PDR, Myanmar, Thailand and Vietnam have also been reviewed. Finally, the author briefly presents Global Groundwater Monitoring Network     

Identification of groundwater potential in hard rock aquifer systems using Remote Sensing,GIS and Magnetic Survey in Veppanthattai,Perambalur, Tamilnadu

Water is an essential natural resource without which life wouldn’t exist. The study aims to identify groundwater potential areas in Vepapanthattai taluk of Perambalur district, Tamil Nadu, India, using analytic hierarchy process (AHP) model. Remote sensing and magnetic parameters have been used to determine the evaluation indicators for groundwater occurrence under the ArcGIS environment. Groundwater occurrence is linked to structural porosity and permeability over the predominantly hard rock terrain, making magnetic data more relevant for locating groundwater potential zones in the research area. NE-SW and NW-SE trending magnetic breaks derived from reduction to pole map are found to be more significant for groundwater exploration. The lineaments rose diagram indicates the general trend of the fracture to be in the NE-SW direction. Assigned normalised criteria weights acquired using the AHP model was used to reclassify the thematic layers. As a result, the taluk’s low, moderate, and high potential zones cover 25.08%, 25.68% and 49.24% of the study area, respectively. The high potential zones exhibit characteristics favourable for groundwater infiltration and storage, with factors as gentle slope of <3°, high lineament densities, magnetic breaks, magnetic low zones as indicative of dykes and cracks, lithology as colluvial deposits and land surface with dense vegetation. The depth of the fracture zones was estimated using power spectrum and Euler Deconvolution method. The groundwater potential mapping results were validated using groundwater level data measured from the wells, which indicated that the groundwater potential zoning results are consistent with the data derived from the real world.     

Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN) model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain. The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity, groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP) algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.     

基于交替迭代算法神经网络评价岩石边坡稳定性

目前边坡工程中常用的稳定性分析方法主要分为极限平衡法和数值分析法2大类，文章对它们各自的主要愿理、特点及其优缺点等进行了阐述。首先，根据经典边坡稳定分析方法存在的局限性，提出有必要建立基于人工神经网络的边坡稳定性预报方法。其次，针对经典算法BP网络存在的某些缺陷，提出了一种交替迭代算法神经网络，以提高其非线性映射能力和泛化能力。交替迭代神经网络算法通过解2个阶数比较低的线性代数方程组，逐步求得连接权值的。以此提高收敛速度，且有利于寻求最优解。作者用FORTRAN语言编制了程序。分析了建立边坡岩体稳定性预测网络模型的建立中应该注意的几个方面。最后，基于已有的40个岩石边坡工程实例进行所建立的神经网络的训练和边坡稳定的预报，结果表明文中所建立的边坡稳定性预报方法具有较高的预报准确度。     

岩土边坡稳定性预报的人工神经网络方法

阐述了经典边坡稳定分析方法的局限性,综合考虑了影响边坡稳定性的因素,建立基于人工神经网络的边坡稳定性预报方法。采用遗传算法优化神经网络的结构,以提高其非线性映射能力和泛化能力,从而,提高预报准确度。基于已有的工程实例训练所建立的神经网络,并对新的边坡稳定性问题进行了预报,预报结果表明,所建立的边坡稳定性预报方法具有较高的预报准确度。     

灰色系统与神经网络组合模型在地下水水位预测中的应用

灰色GM(1,1)模型与人工BP神经网络对于预测非线性数列变化趋势都具有很好的适用性,但同其他预测方法一样也存在各自的局限性。本文采用灰色GM(1,1)模型与人工神经网络相结合的方法,对GM(1,1)模型预测结果进行了修正。以收集到的某地区1996～2006年的地下水水位埋深数据为算例,计算结果表明,经人工神经网络修正后的灰色系统的预测值比原预测值的预测精度有了很大提高。     

基于MatLab的神经网络在江苏矿山地质环境评估中的应用

神经网络作为一种新的方法体系,具有分布并行处理、非线性映射、自适应学习和鲁棒容错等特性,在模式识别、控制优化和智能信息处理等方面有着广泛的应用。利用MatLab的神经网络工具箱,建立了江苏矿山地质环境质量的评估模型,评估结果经过实际验证,具有较高的可信度和实用性。