Search results for: deep aquifer

Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui

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Recommender systems have become increasingly popular in recent years, and are utilized in numerous areas. Nowadays many web services provide several information for users and recommender systems have been developed as critical element of these web applications to predict choice of preference and provide significant recommendations. With the help of the advantage of deep learning in modeling different types of data and due to the dynamic change of user preference, building a deep model can better understand users demand and further improve quality of recommendation. In this paper, deep neural network models for recommender system are evaluated. Most of deep neural network models in recommender system focus on the classical collaborative filtering user-item setting. Deep learning models demonstrated high level features of complex data can be learned instead of using metadata which can significantly improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender system have not been fully exploited and still a lot of improvements can be done both in collaborative and content-based approach while considering different contextual factors.

Keywords: big data, decision making, deep learning, recommender system

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Authors: M. Abdallah

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Urban development requires deep excavations near buildings and other structures. Deep excavation has become more a necessity for better utilization of space as the population of the world has dramatically increased. In Lebanon, some urban areas are very crowded and lack spaces for new buildings and underground projects, which makes the usage of underground space indispensable. In this paper, a numerical modeling is performed using the finite element method to study the deep excavation-diaphragm wall soil-structure interaction in the case of nonlinear soil behavior. The study is focused on a comparison of the results obtained using different support systems. Furthermore, a parametric study is performed according to the remoteness of the structure.

Keywords: deep excavation, ground anchors, interaction soil-structure, struts

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Authors: Benhammadi Hocine, Fehdi Chemseddine, Chaffai Hicham

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The study site is the plateau Cheria, a city in south eastern Algeria (Tebessa) thanks to its structure perched syncline is the region of Tebessa a real water tower. Special rates provided by some boreholes and wells around the city Cheria have long been led to believe that the reserves were virtually limitless. The investigations carried out in this region have located karstified limestone areas at depth of 100 meters of the carbonate formation. During the last two decades a rainfall deficit has increased the effect of drought has caused an increase in flow from this aquifer. The effect on water resources is a significant and progressive reduction of the static level of the karst aquifer. The qualitative aspect has also been marked by degradation. This climate variability marked by the decade of drought (1990/2000) has had the effect on the local population, a forced change of their activity primarily agricultural. Abandoning agro pastoral mode due to prolonged drought, populations chose agriculture maraichère consumer a lot of water, this increasing the depletion of water resources. This change in activity was accompanied by a rural exodus to urban areas. The result has led to an increase in population in the urban areas, this has resulted in an increase in water demand and an increase in emissions (waste water). Uncontrolled discharges contribute to pollute a little more groundwater. The second consequence is type Geotechnical, it is the appearance of sinkholes, results of the alternating periods of drought and violent floods. Sinkholes are a real concern for the management and urban development. An interdisciplinary contribution (geology, hydrology, climatology and management) is essential to reduce or avoid impacts in different sectors.

Keywords: aquifer, carbonate formation, drought, exodus, resources, chéria, Algéria

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Palingba Aimé Marie Doilkom, Mahamadou Koïta, Jean-michel Vouillamoz, Angelbert Biaou

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Most African populations rely on groundwater in rural areas for their consumption. Indeed, in the face of climate change and strong demographic growth, groundwater, particularly in the basement, is increasingly in demand. The question of the sustainability of water resources in this type of environment is therefore becoming a major issue. Groundwater recharge can be natural or artificial. Unlike natural recharge, which often results from the natural infiltration of surface water (e.g. a share of rainfall), artificial recharge consists of causing water infiltration through appropriate developments to artificially replenish the water stock of an aquifer. Artificial recharge is, therefore, one of the measures that can be implemented to secure water supply, combat the effects of climate change, and, more generally, contribute to improving the quantitative status of groundwater bodies. It is in this context that the present research is conducted with the aim of developing artificial recharge in order to contribute to the sustainability of basement aquifers in a context of climatic variability and constantly increasing water needs of populations. In order to achieve the expected results, it is therefore important to determine the characteristics of the infiltration basins and to identify the areas suitable for their implementation. The geometry of the aquifer was reproduced, and the hydraulic properties of the aquifer were collected and characterized, including boundary conditions, hydraulic conductivity, effective porosity, recharge, Van Genuchten parameters, and saturation indices. The aquifer of the Sanon experimental site is made up of three layers, namely the saprolite, the fissured horizon, and the healthy basement. Indeed, the saprolite and the fissured medium were considered for the simulations. The first results with FEFLOW model show that the water table reacts continuously for the first 100 days before stabilizing. The hydraulic charge increases by an average of 1 m. The further away from the basin, the less the water table reacts. However, if a variable hydraulic head is imposed on the basins, it can be seen that the response of the water table is not uniform over time. The lower the basin hydraulic head, the less it affects the water table. These simulations must be continued by improving the characteristics of the basins in order to obtain the appropriate characteristics for a good recharge.

Keywords: basement area, FEFLOW, infiltration basin, MAR

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Authors: Mohamed Redha Menani

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The Northern Saharan aquifer system “SASS” shared by Algeria, Libya, and Tunisia, covers a surface of about 1 100 000 km². It is composed of superposed aquifers; the upper one is the “Continental terminal – CT” (Eocene calcareous formation) situated at 400 m depth in average, while the” Continental Intercalaire – CI”(clay sands from Albian to Lower Cretaceous) is generally at 1500 m depth. This aquifer system is situated in a dry zone with a very weak current recharge but with a non-renewable big volume stored, estimated between 20 000 and 31 000 km³. From 1970 to nowadays, the exploitation of the SASS has increased from 0.6 to more than 2.5 km³/year. This situation provoked risks of water salinisation, reduction of the artesianisme, an increase of drawdowns, etc. which seriously threaten the sustainable socioeconomic development engaged in the SASS zone. Face the water shortage induced by the alarming dryness noted these last years, particularly in the MENA region, the joint management of this system by the three concerned countries, engaged for many years, needs a long-term strategy of integrated water resources management to meet the expected socio-economic goals projected not only in the SASS zone but also in other places, by water transfers. The sustainable management of this extensive aquifer system, aiming to satisfy various needs not only in the areas covered by the SASS but also in other areas through hydraulic transfers, can only be considered if this management is genuinely coordinated, incorporating schemes that primarily address the major constraint of climate change, which has been observed worldwide over the past two decades and is intensifying. In this particular climate context, management schemes must necessarily target several aspects, including (i) Updating the state of water resource exploitation in the SASS. (ii) Guiding agricultural usage as the primary consumer to ensure significant water savings. (iii) Constant monitoring through a network of piezometers to control the physicochemical parameters of the exploited aquifers. (iv) Other aspects related to governance within the framework of integrated management must also be taken into consideration, particularly environmental aspects and conflict resolution. However, problems, especially political ones as currently seen in Libya, may limit or at least disrupt the prospects of coordinated and sustainable management of this aquifer system, which is vital for the three countries.

Keywords: transboundary water resources, SASS, governance, climatic changes

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Authors: Jiaqi Huang, Yuheng Wang

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Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks.

Keywords: automated theorem proving, premises selection, deep learning, interpreting deep learning

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Authors: Radzuan Junin, Dayang Zulaika A. Hasbollah

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The increasing amount of greenhouse gasses in the atmosphere recently has become one of the discussed topics in relation with world’s concern on climate change. Developing countries’ emissions (such as Malaysia) are now seen to surpass developed country’s emissions due to rapid economic development growth in recent decades. This paper presents the potential storage sites suitability and storage capacity assessment for CO2 sequestration in sedimentary basins of Malaysia. This study is the first of its kind that made an identification of potential storage sites and assessment of CO2 storage capacity within the deep saline aquifers in the country. The CO2 storage capacity in saline formation assessment was conducted based on the method for quick assessment of CO2 storage capacity in closed, and semi-closed saline formations modified to suit the geology setting of Malaysia. Then, an integrated approach that involved geographic information systems (GIS) analysis and field data assessment was adopted to provide the potential storage sites and its capacity for CO2 sequestration. This study concentrated on the assessment of major sedimentary basins in Malaysia both onshore and offshore where potential geological formations which CO2 could be stored exist below 800 meters and where suitable sealing formations are present. Based on regional study and amount of data available, there are 14 sedimentary basins all around Malaysia that has been identified as potential CO2 storage. Meanwhile, from the screening and ranking exercises, it is obvious that Malay Basin, Central Luconia Province, West Baram Delta and Balingian Province are respectively ranked as the top four in the ranking system for CO2 storage. 27% of sedimentary basins in Malaysia were evaluated as high potential area for CO2 storage. This study should provide a basis for further work to reduce the uncertainty in these estimates and also provide support to policy makers on future planning of carbon capture and sequestration (CCS) projects in Malaysia.

Keywords: CO₂ storage, deep saline aquifer, GIS, sedimentary basin

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Authors: Mannal Tariq

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Construction industry is making progress at a high pace. The trend of the world is getting more biased towards the high rise buildings. Deep beams are one of the most common elements in modern construction having small span to depth ratio. Deep beams are mostly used as transfer girders. This experimental study consists of 16 reinforced concrete (RC) deep beams. These beams were divided into two groups; A and B. Groups A and B consist of eight beams each, having 381 mm (15 in) and 457 mm (18 in) depth respectively. Each group was further subdivided into four sub groups each consisting of two identical beams. Each subgroup was comprised of solid/control beam (without opening), opening above neutral axis (NA), at NA and below NA. Except for control beams, all beams with openings were strengthened with carbon fibre reinforced polymer (CFRP) vertical strips. These eight groups differ from each other based on depth and location of openings. For testing sake, all beams have been loaded with two symmetrical point loads. All beams have been designed based on strut and tie model concept. The outcome of experimental investigation elaborates the difference in the shear behaviour of deep beams based on depth and location of circular openings variation. 457 mm (18 in) deep beam with openings above NA show the highest strength and 381 mm (15 in) deep beam with openings below NA show the least strength. CFRP sheets played a vital role in increasing the shear capacity of beams.

Keywords: CFRP, deep beams, openings in deep beams, strut and tie modal, shear behaviour

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Authors: Seyed Abolhasan Naeini, Saeideh Mohammadi

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Embankment settlement on soft clays has always been problematic due to the high compaction and low shear strength of the soil. Deep soil mixing and geosynthetics are two soil improvement methods in such fields. Here, a numerical study is conducted on the embankment performance on the soft ground improved by deep soil mixing columns and geosynthetics based on the data of a real project. For this purpose, the finite element method is used in the Plaxis 2D software. The Soft Soil Creep model considers the creep phenomenon in the soft clay layer while the Mohr-Columb model simulates other soil layers. Results are verified using the data of an experimental embankment built on deep mixing columns. The effect of depth and diameter of deep mixing columns and the stiffness of geogrid on the vertical and horizontal movements of embankment on clay subsoil will be investigated in the following.

Keywords: PLAXIS 2D, embankment settlement, horizontal movement, deep soil mixing column, geogrid

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Authors: Ruqaya H. Aljabery

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Reinforced Concrete (RC) deep beams are one of the main critical structural elements in terms of safety since significant loads are carried in a short span. The shear capacity of these sections cannot be predicted accurately by the current design codes like ACI and EC2; thus, they must be investigated. In this research, non-linear behavior of RC deep beams strengthened in shear with Fiber Reinforced Polymer (FRP) strips, and the efficiency of FRP in terms of enhancing the shear capacity in RC deep beams are examined using Finite Element Analysis (FEA), which is conducted using the software ABAQUS. The effect of several parameters on the shear capacity of the RC deep beam are studied in this paper as well including the effect of the cross-sectional area of the FRP strip and the shear reinforcement area to the spacing ratio (As/S), and it was found that FRP enhances the shear capacity significantly and can be a substitution of steel stirrups resulting in a more economical design.

Keywords: Abaqus, concrete, deep beam, finite element analysis, FRP, shear strengthening, strut-and-tie

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Authors: Nitin Shravan, Sudarsun Santhiappan, B. Sivaselvan

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Subsection identification, in the context of Electronic Health Records (EHRs), is identifying the important sections for down-stream tasks like auto-coding. In this work, we classify the text present in EHRs according to their information, using machine learning and deep learning techniques. We initially describe briefly about the problem and formulate it as a text classification problem. Then, we discuss upon the methods from the literature. We try two approaches - traditional feature extraction based machine learning methods and deep learning methods. Through experiments on a private dataset, we establish that the deep learning methods perform better than the feature extraction based Machine Learning Models.

Keywords: deep learning, machine learning, semantic clinical classification, subsection identification, text classification

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Mohammad Shameel, Peter K. K. Loh, James H. Ng

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Cybercrime is a new category of crime which poses a different challenge for crime investigators and incident responders. Attackers can mask their identities using a suite of tools and with the help of the deep web, which makes them difficult to track down. Scouring the deep web manually takes time and is inefficient. There is a growing need for a tool to scour the deep web to obtain useful evidence or intel automatically. In this paper, we will explain the background and motivation behind the research, present a survey of existing research on related tools, describe the design of our own crime/threat intelligence hunting tool prototype, demonstrate its capability with some test cases and lastly, conclude with proposals for future enhancements.

Keywords: cybercrime, deep web, threat intelligence, web crawler

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Authors: Hana' Al-Ghanim, Mu'tasim Abdel-Jaber, Maha Alqam

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This experimental investigation deals with shear strengthening of reinforced concrete (RC) deep beams using the externally bonded carbon fiber-reinforced polymer (CFRP) composites. The current study, therefore, evaluates the effectiveness of four various configurations for shear strengthening of deep beams with two different types of CFRP materials including sheets and laminates. For this purpose, a total of 10 specimens of deep beams were cast and tested. The shear performance of the strengthened beams is assessed with respect to the cracks’ formation, modes of failure, ultimate strength and the overall stiffness. The obtained results demonstrate the effectiveness of using the CFRP technique on enhancing the shear capacity of deep beams; however, the efficiency varies depending on the material used and the strengthening scheme adopted. Among the four investigated schemes, the highest increase in the ultimate strength is recorded by using the continuous wrap of two layers of CFRP sheets, exceeding a value of 86%, whereas an enhancement of about 36% is achieved by the inclined CFRP laminates.

Keywords: deep beams, laminates, shear strengthening, sheets

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Authors: Fofie Kokea Ariane Darolle, Gouet Daniel Hervé, Koumetio Fidèle, Yemele David

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The electrical resistivity method is successfully used in this paper in order to have a clearer picture of the subsurface of the North-East ofFiguil in northern Cameroon. It is worth noting that this method is most often used when the objective of the study is to image the shallow subsoils by considering them as a set of stratified ground layers. The problem to be solved is very often environmental, and in this case, it is necessary to perform an inversion of the data in order to have a complete and accurate picture of the parameters of the said layers. In the case of this work, thirty-three (33) Schlumberger VES have been carried out on an irregular grid to investigate the subsurface of the study area. The 1D inversion applied as a preliminary modeling tool and in correlation with the mechanical drillings results indicates a complex subsurface lithology distribution mainly consisting of marbles and schists. Moreover, the quasi-3D inversion with lateral constraint shows that the misfit between the observed field data and the model response is quite good and acceptable with a value low than 10%. The method also reveals existence of two water bearing in the considered area. The first is the schist or weathering aquifer (unsuitable), and the other is the marble or the fracturing aquifer (suitable). The final quasi 3D inversion results and geological models indicate proper sites for groundwaters prospecting and for mining exploitation, thus allowing the economic development of the study area.

Keywords: electrical resistivity method, 1D inversion, quasi 3D inversion, groundwaters, mining

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Authors: Xuewei Li, Jingfeng Yuan, Jianliang Zhou

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The 21st century has been called the century of human exploitation of underground space. Due to the characteristics of large quantity, tight schedule, low safety reserve and high uncertainty of deep foundation pit engineering, accidents frequently occur in deep foundation pit engineering, causing huge economic losses and casualties. With the successful application of information technology in the construction industry, building information modeling has become a research hotspot in the field of architectural engineering. Therefore, the application of building information modeling (BIM) and other information communication technologies (ICTs) in construction safety management is of great significance to improve the level of safety management. This research summed up the mechanism of the deep foundation pit engineering accident through the fault tree analysis to find the control factors of deep foundation pit engineering safety management, the deficiency existing in the traditional deep foundation pit construction site safety management. According to the accident cause mechanism and the specific process of deep foundation pit construction, the hazard information of deep foundation pit engineering construction site was identified, and the hazard list was obtained, including early warning information. After that, the system framework was constructed by analyzing the early warning information demand and early warning function demand of the safety management system of deep foundation pit. Finally, the safety management system of deep foundation pit construction site based on BIM through combing the database and Web-BIM technology was developed, so as to realize the three functions of real-time positioning of construction site personnel, automatic warning of entering a dangerous area, real-time monitoring of deep foundation pit structure deformation and automatic warning. This study can initially improve the current situation of safety management in the construction site of deep foundation pit. Additionally, the active control before the occurrence of deep foundation pit accidents and the whole process dynamic control in the construction process can be realized so as to prevent and control the occurrence of safety accidents in the construction of deep foundation pit engineering.

Keywords: Web-BIM, safety management, deep foundation pit, construction

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Emna Bahri Hammami, Zammouri Mounira, Tarhouni Jamila

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The Nadhour Saouaf Sisseb El Alem (NSSA) system comprises some of the most intensively exploited aquifers in central Tunisia. Since the 1970s, the growth in economic productivity linked to intensive agriculture in this semiarid region has been sustained by increasing pumping rates of the system’s groundwater. Exploitation of these aquifers has increased rapidly, ultimately causing their depletion. With the aim to better understand the behavior of the aquifer system and to predict its evolution, the paper presents a finite difference model of the groundwater flow and solute transport. The model is based on the Groundwater Modeling System (GMS) and was calibrated using data from 1970 to 2010. Groundwater levels observed in 1970 were used for the steady-state calibration. Groundwater levels observed from 1971 to 2010 served to calibrate the transient state. The impact of pumping discharge on the evolution of groundwater levels was studied through three hypothetical pumping scenarios. The first two scenarios replicated the approximate drawdown in the aquifer heads (about 17 m in scenario 1 and 23 m in scenario 2 in the center of NSSA) following an increase in pumping rates by 30% and 50% from their current values, respectively. In addition, pumping was stopped in the third scenario, which could increase groundwater reserves by about 7 Mm3/year. NSSA groundwater reserves could be improved considerably if the pumping rules were taken seriously.

Keywords: pumping, depletion, groundwater modeling system GMS, Nadhour Saouaf

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Authors: Mayank Sharma

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Groundwater has been a matter of great concern in the past years due to the depletion in the water table. This has resulted from the over-exploitation of groundwater resources. Sub-surface exploration of groundwater is a great way to identify the groundwater potential of an area. Thus, in order to meet the water needs for irrigation in the study area, there was a need for a tube well to be installed. Therefore, a Geophysical investigation was carried out to find the most suitable point of drilling and sinking of tube well that encounters an aquifer. Hence, an electrical resistivity survey of geophysical exploration was used to know the aquifer zones of the area. The Vertical Electrical Sounding (VES) method was employed to know the subsurface geology of the area. Seven vertical electrical soundings using Schlumberger electrode array were carried out, having the maximum AB electrode separation of 700m at selected points in Ayma, Kharagpur-1 block of Paschim Midnapore district, West Bengal. The VES was done using an IGIS DDR3 Resistivity meter up to an approximate depth of 160-180m. The data was interpreted, processed and analyzed. Based on all the interpretations using the direct method, the geology of the area at the points of sounding was interpreted. It was established that two deeper clay-sand sections exist in the area at a depth of 50-70m (having resistivity range of 40-60ohm-m) and 70-160m (having resistivity range of 25-35ohm-m). These aquifers will provide a high yield of water which would be sufficient for the desired irrigation in the study area.

Keywords: VES method, Schlumberger method, electrical resistivity survey, geophysical exploration

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Authors: Mohamad Kassab, Amal El Fallah Seghrouchni, Frederic Barbaresco, Raed Abu Zitar

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Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented.

Keywords: drones detection, deep learning, birds versus drones, precision of detection, AdderNet

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Mingmin Pan, Xianpei Li, Wenli Hu

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Glaucoma is impelled by optic nerve mutilation habitually represented as cupping and visual field injury frequently with an arcuate pattern of mid-peripheral loss, subordinate to retinal ganglion cell damage and death. Glaucoma is the second foremost cause of blindness and the chief cause of permanent blindness worldwide. Consequently, all-embracing study into the analysis and empathy of glaucoma is happening to escort deep learning based neural network intrusions to deliberate this substantial optic neuropathy. This paper advances an ophthalmic hashing based supervision of glaucoma and corneal disorders preeminent on deep graphical model. Ophthalmic hashing is a newly proposed method extending the efficacy of visual hash-coding to predict glaucoma corneal disorder matching, which is the faster than the existing methods. Deep graphical model is proficient of learning interior explications of corneal disorders in satisfactory time to solve hard combinatoric incongruities using deep Boltzmann machines.

Keywords: corneal disorders, deep Boltzmann machines, deep graphical model, glaucoma, neural networks, ophthalmic hashing

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Authors: Ilham Aji Dermawan, M. Sapari Dwi Hadian, R. Irvan Sophian, Iyan Haryanto

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The study of the geological structure in the surrounding area of Cidaun, Cianjur Regency, West Java Province, Indonesia was conducted around the southern coast of Java Island. This study aims to determine the potentially structural trap deposits of freshwater resources in the study area, according to that the study area is an area directly adjacent to the beach, where the water around it did not seem fresh and brackish due to the exposure of sea water intrusion. This study uses the method of geomorphological analysis and geological mapping by taking the data directly in the field within 10x10 km of the research area. Geomorphological analysis was done by calculating the watershed drainage density value and roundness of watershed value ratio. The goal is to determine the permeability of the sub-soil conditions, rock constituent, and the flow of surface water. While the field geological mapping aims to take the geological structure data and then will do the reconstruction to determine the geological conditions of research area. The result, from geomorphology aspects, that the considered area of potential groundwater consisted of permeable surface material, permeable sub-soil, and low of water run-off flow. It is very good for groundwater recharge area. While the results of geological reconstruction after conducted of geological mapping is joints that present were initiated for the Cipandak Fault that cuts Cipandak River. That fault across until the Cibako Syncline fold through the Cibako River. This syncline is expected to place of influent groundwater aquifer. The tip of Cibako River then united with Cipandak River, where the Cipandak River extends through Cipandak Syncline fold axis in the southern regions close to its estuary. This syncline is expected to place of influent groundwater aquifer too.

Keywords: geological structure, groundwater, hydrogeology, influent aquifer, structural trap

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Authors: Sascha E. Oswald, Jan Busch

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The in-situ dechlorination of contamination by chlorinated solvents in groundwater via zero-valent iron (nZVI) is potentially an efficient and prompt remediation method. A key requirement is that nZVI has to be introduced in the subsurface in a way that substantial quantities of the contaminants are actually brought into direct contact with the nZVI in the aquifer. Thus it could be a more flexible and precise alternative to permeable reactive barrier techniques using granular iron. However, nZVI are often limited by fast agglomeration and sedimentation in colloidal suspensions, even more so in the aquifer sediments, which is a handicap for the application to treat source zones or contaminant plumes. Colloid-supported nZVI show promising characteristics to overcome these limitations and Carbo-Iron Colloids is a newly developed composite material aiming for that. The nZVI is built onto finely ground activated carbon of about a micrometer diameter acting as a carrier for it. The Carbo-Iron Colloids are often suspended with a polyanionic stabilizer, and carboxymethyl cellulose is one with good properties for that. We have investigated the transport behavior of Carbo-Iron Colloids (CIC) on different scales and for different conditions to assess its mobility in aquifer sediments as a key property for making its application feasible. The transport properties were tested in one-dimensional laboratory columns, a two-dimensional model aquifer and also an injection experiment in the field. Those experiments were accompanied by non-invasive tomographic investigations of the transport and filtration processes of CIC suspensions. The laboratory experiments showed that a larger part of the CIC can travel at least scales of meters for favorable but realistic conditions. Partly this is even similar to a dissolved tracer. For less favorable conditions this can be much smaller and in all cases a particular fraction of the CIC injected is retained mainly shortly after entering the porous medium. As field experiment a horizontal flow field was established, between two wells with a distance of 5 meters, in a confined, shallow aquifer at a contaminated site in North German lowlands. First a tracer test was performed and a basic model was set up to define the design of the CIC injection experiment. Then CIC suspension was introduced into the aquifer at the injection well while the second well was pumped and samples taken there to observe the breakthrough of CIC. This was based on direct visual inspection and total particle and iron concentrations of water samples analyzed in the laboratory later. It could be concluded that at least 12% of the CIC amount injected reached the extraction well in due course, some of it traveling distances larger than 10 meters in the non-uniform dipole flow field. This demonstrated that these CIC particles have a substantial mobility for reaching larger volumes of a contaminated aquifer and for interacting there by their reactivity with dissolved contaminants in the pore space. Therefore they seem suited well for groundwater remediation by in-situ formation of reactive barriers for chlorinated solvent plumes or even source removal.

Keywords: carbo-iron colloids, chlorinated solvents, in-situ remediation, particle transport, plume treatment

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Authors: A. Sengouga, Y. Imessaoudene, A. Semar, B. Mouhouche, M. Kadir

Abstract:

Biskra or Zibans, is located in a structural transition zone between the chain of the Saharan Atlas Mountains and the Sahara. It is an arid region where the superficial water resource is the mild, hence the importance of the lithological description and the evaluation of aquifers rock’s volumes, which are highly dependent on the mobilized water contained in the various reservoirs (Quaternary, Mio-Pliocene, Eocene and Continental intercalary). Through a data synthesis which is particularly based on stratigraphic logs of drilling, the description of aquifers heterogeneity and the determining of the spatial variability of aquifer appearance became possible, by using geostatistical analysis, which allowed the representation of the aquifer thicknesses mapping and their space variation. The different thematic maps realized focus on drilling position, the substratum shape and finally the aquifers thicknesses of the region. It is found that the high density of water points especially these of drilling points are superposed on the hydrologic reservoirs with significant thicknesses.

Keywords: log stratigraphic ArcGIS 10, geometry of aquifers, rocks reservoir volume, Biskra

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Authors: Wentian Shi, Daming Shi, Maysam Orouskhani, Feng Tian

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Whereas currently the most prevalent deep learning methods require a large amount of data for training, few-shot learning tries to learn a model from limited data without extensive retraining. In this paper, we present a loss function based on triplet loss for solving few-shot problem using metric based learning. Instead of setting the margin distance in triplet loss as a constant number empirically, we propose an adaptive margin distance strategy to obtain the appropriate margin distance automatically. We implement the strategy in the deep siamese network for deep metric embedding, by utilizing an optimization approach by penalizing the worst case and rewarding the best. Our experiments on image recognition and co-segmentation model demonstrate that using our proposed triplet loss with adaptive margin distance can significantly improve the performance.

Keywords: few-shot learning, triplet network, adaptive margin, deep learning

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Authors: A. Taheri Tizro, Rojin Fasihi

Abstract:

Geoelectrical method is one of the most effective tools in determining subsurface lithological layers. The electromagnetic method is also a newer method that can play an important role in determining and separating subsurface layers with acceptable accuracy. In the present research, 10 electromagnetic soundings were collected in the upstream of 5 karstic springs of Famaseb, Faresban, Ghale Baroodab, Gian and Gonbad kabood in Nahavand plain of Hamadan province. By using the emerging data, the belectromagnetic logs were prepared at different depths and compared with 5 logs of the geoelectric method. The comparison showed that the value of NRMSE in the geoelectric method for the 5 springs of Famaseb, Faresban, Ghale Baroodab, Gian and Gonbad kabood were 7.11, 7.50, respectively. It is 44.93, 3.99, and 2.99, and in the electromagnetic method, the value of this coefficient for the investigated springs is about 1.4, 1.1, 1.2, 1.5, and 1.3, respectively. In addition to the similarity of the results of the two methods, it is found that, the accuracy of the electromagnetic method based on the NRMSE value is higher than the geoelectric method. The advantage of the electromagnetic method compared to geoelectric is on less time consuming and its cost prohibitive. The depth to water table is the final result of this research work , which showed that in the springs of Famaseb, Faresban, Ghale Baroodab, Gian and Gonbad kabood, having depth of about 6, 20, 10, 2 36 meters respectively. The maximum thickness of the aquifer layer was estimated in Gonbad kabood spring (36 meters) and the lowest in Gian spring (2 meters). These results can be used to identify the water potential of the region in order to better manage water resources.

Keywords: karst spring, geoelectric, aquifer layers, nahavand

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Kruti Jaruriya

Abstract:

Distribution of arsenic (As) and its compound and related toxicology are serious concerns. This is particularly so since millions worldwide are suffering from toxicity due to drinking of As-contaminated groundwater. The Bengal delta plain, formed by the Ganga– Padma–Meghna–Brahmaputra river basin, covering several districts of West Bengal, India and Bangladesh is considered as the worst As affected alluvial basin. However, some equally affected, if not more, areas are emerging in upper Brahmaputra plains. The present study was carried out to examine As contamination trends in the worst affected part of Assam, India. Arsenic (As) mobilization to the groundwater of Brahmaputra floodplains was investigated in Titabor, Jorhat District, located in the North Eastern part of India. The groundwater and the aquifer geochemistry were characterized. The groundwater is characterized by high dissolved Fe, Mn, and HCO-3 and low concentrations of NO-3 and SO2-4 indicating anoxic conditions prevailing in the groundwater. Fifty groundwater samples collected from shallow and deep tubewells of Titabor, Jorhat district (Assam) were examined. Along with total As, examination of concentration levels of other key parameters, viz., pH, EC, Fe, Mn , Mg2+, Ca2+, Na+, K+, PO43- , HCO-3 , NO3- ,Cl - and SO42- was also carried out. In respect to the permissible guideline of World Health Organization (WHO: As 0.01 ppm, Fe 1.0 ppm, and Mn 0.3 ppm for potable water), the range of As concentration in the groundwater varied from 0.014 to 0.604 mg/L with mean concentration 0.184 mg/L. The present study showed that out of the 50 groundwater samples,100%, 54%, and 42% were found contaminated with higher metal contents (for total As, Fe, and Mn, respectively). The results of hydrogeochemical study revealed that the reductive dissolution of MnOOH and FeOOH represents an important mechanism of arsenic release in the study area along with major cations playing an important role in leaching of As into the groundwater. Arsenic released by oxidation of pyrite, as water levels are drawn down and air enters the aquifer, contributes negligibly to the problem of As pollution. Identification of the mechanism of As release to groundwater helps to provide a framework to guide the placement of new water wells so that they will have acceptable concentrations of As.

Keywords: arsenic, assam, brahmaputra floodplain, groundwater, hydrogeochemistry

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