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Prediction of groundwater level fluctuations under climate change based on machine learning algorithms in the Mashhad Aquifer, Iran

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JOURNAL OF WATER AND CLIMATE CHANGE 2023年第3期14卷 1039-1059页

作者： Panahi, Ghasem Hassanzadeh Eskafi, Mahya Faridhosseini, Alireza Khodashenas, Saeed Reza Rohani, Abbas Ferdowsi Univ Mashhad FUM Agr Fac Water Sci & Engn Dept Mashhad Iran Ferdowsi Univ Mashhad FUM Agr Fac Biosyst Engn Mashhad Iran

Due to population growth in recent years and climate change in arid and semi-arid regions, the lack of rainfall and the reduction of surface water flows required in various sectors, monitoring, and projection of the climate change impact on the groundwater level (GWL) in the future are vital in the management and control of these resources. The purpose of this study is the projection of climate change's impact on the GWL fluctuations in the Mashhad aquifer during the future period (2022-2064). In the first step, the climatic variables using the ACCESS-CM2 model under the Shared Socio-economic Pathway (SSP) 5-8.5 scenario were extracted. In the second step, different machine learning algor-ithms were used to predict the GWL fluctuations under climate change in the future. Our results point out that temperatures and evaporation will increase in the autumn season and precipitation will decrease by 26% in the future in the Mashhad aquifer. The results showed that the Radial Basis Function Neural Network (RBFNN) model had an excellent performance in predicting the GWL compared to other models. Based on the result of the RBF model, the GWL will decrease by 6.60 m under the SSP5-8.5 scenario in the future.

关键词： climate change CMIP6 model groundwater level machine learning algorithms

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Using machine learning algorithms to map the groundwater recharge potential zones

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2020年 265卷 110525-110525页

作者： Pourghasemi, Hamid Reza Sadhasivam, Nitheshnirmal Yousefi, Saleh Tavangar, Shahla Nazarlou, Hamid Ghaffari Santosh, M. Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Bharathidasan Univ Sch Earth Sci Dept Geog Tiruchirappalli 620024 Tamil Nadu India AREEO Soil Conservat & Watershed Management Res Dept Chaharmahal & Bakhtiari Agr & Nat Resources Res & Shahrekord Iran Tarbiat Modare Univ Fac Nat Resources & Marine Sci Dept Watershed Management Engn Tehran Iran Howard Univ Dept Civil & Environm Engn Washington DC 20059 USA China Univ Geosci Beijing Sch Earth Sci & Resources Beijing 100083 Peoples R China Univ Adelaide Dept Earth Sci Adelaide SA 5005 Australia

Groundwater recharge is indispensable for the sustainable management of freshwater resources, especially in the arid regions. Here we address some of the important aspects of groundwater recharge through machine learning algorithms (MLAs). Three MLAs including, SVM, MARS, and RF were validated for higher prediction accuracies in generating groundwater recharge potential maps (GRPMs). Accordingly, soil permeability samples were prepared and are arbitrarily grouped into training (70%) and validation (30%) samples. The GRPMs are generated using sixteen effective factors, such as elevation (denoted using a digital elevation model;DEM), aspect, slope angle, TWI (topographic wetness index), fault density, MRVBF (multiresolution index of valley bottom flatness), rainfall, lithology, land use, drainage density, distance from rivers, distance from faults, annual ETP (evapo-transpiration), minimum temperature, maximum temperature, and rainfall 24-hr. Subsequently, the VI (variables importance) is assessed based on the LASSO algorithm. The GRPMs of three MLAs were validated using the ROC-AUC (receiver operating characteristic-area under curve) and various techniques including true positive rate (TPR), false positive rate (FPR), F-measures, fallout, sensitivity, specificity, true skill statistics (TSS), and corrected classified instances (CCI). Based on the validation, the RF algorithm performed better (AUC = 0.987) than the SVM (AUC = 0.963) and the MARS algorithm (AUC = 0.962). Furthermore, the accuracy of these MLAs are included in excellent class, based on the ROC curve threshold. Our case study shows that the GRPMs are potential guidelines for decision-makers in drafting policies related to the sustainable management of the groundwater resources.

关键词： Groundwater recharge machine learning algorithms Variable importance LASSO

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A Smart Gas Sensor Using machine learning algorithms: Sensor Types Based on IED Configurations, Fabrication Techniques, Algorithmic Approaches, Challenges, Progress, and Limitations: A Review

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IEEE SENSORS JOURNAL 2023年第11期23卷 11336-11355页

作者： Nasri, Abdelghaffar Boujnah, Aicha Boubaker, Aimen Kalboussi, Adel Icam F-31300 Toulouse France Univ Monastir Dept Phys Fac Sci Monastir 5000 Tunisia

Over the past decade, machine learning (ML) and artificial intelligence (AI) have attracted great interest in research and various practical applications. Currently, smart, fast, and high sensitivity with excellent selectivity are becoming increasingly interesting due to the high need for environmental safety and medical applications. The main challenge is to improve sensor selectivity, which requires the combination of interdisciplinary research areas to successfully develop smart gas/chemical sensing devices with better performance. In this review, we present a few principles of gas sensing based on low-cost interdigital electrodes (IDEs), such as electrochemical, resistive, capacitive, and acoustic sensors. In addition, the most important current methods for improving gas sensing performance, the different materials, the different techniques used to fabricate IDE gas sensors, and their advantages and limitations are presented. In addition, a comparison between different ML and AI algorithms for pattern recognition and classification algorithms is also discussed. The discussion then establishes application cases of smart ML algorithms, which provide efficient data processing methods, for the design of smart gas sensors that are highly selective. In addition, the challenges and limitations of ML in gas sensor applications are critically discussed. The study shows the importance of ML with the need for structural optimization to develop and improve smart, sensitive, and selective sensors.

关键词： Sensors Gas detectors machine learning Intelligent sensors Electrodes Sensor phenomena and characterization machine learning algorithms algorithms IDEs structures machine learning (ML) materials selectivity sensitivity smart gas sensor

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Real-time tool condition monitoring with the internet of things and machine learning algorithms

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年

作者： Mohanraj, T. Bharath, R. Sai Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore India

machine learning and the Internet of Things (IoT) has an essential role in machine health monitoring and maintenance. An intelligent system that can predict the tool condition is created by collecting data from various sensors. With the rich insights derived from the collected data and integrated through the IoT system, predictive analytics is gaining prominence in Tool Condition Monitoring systems (TCMs). The research reported in this paper uses an accelerometer sensor and a microphone positioned on the spindle head to collect vibration and sound signals associated with eight different tool conditions. Then, the statistical and wavelet features are extracted from the collected signals. The T-test is employed to identify the dominant features. These dominant features are used to train the various machine learning classifiers to predict the tool condition. Finally, the statistical features from sound and vibration are combined and trained to explore sensor fusion techniques. The Ensemble Trees is selected as the best-performing algorithm for online IoT-based TCMs. This model predicts tool conditions with an accuracy of 98% and provides alert messages and indications through Thingspeak. By integrating multiple sensors and employing advanced analytical techniques, this approach aims to improve the accuracy and effectiveness of TCMs, enabling proactive maintenance and optimization of tool usage.

关键词： CNC milling TCMs IoT machine learning algorithms sensor fusion

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Prediction of spring agricultural drought using machine learning algorithms in the southern Songnen Plain, China

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LAND DEGRADATION & DEVELOPMENT 2023年第13期34卷 3836-3849页

作者： Chen, Xiuxue Li, Xiaofeng Jiang, Bo Su, Jiajia Zheng, Xingming Wang, Guangrui Chinese Acad Sci Northeast Inst Geog & Agroecol 4888 Shengbei St Changchun 130102 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun Jingyuetan Remote Sensing Expt Stn Jilin Daan Agroecosyst Natl Observat Res Stn Changchun Jilin Peoples R China Moisture Monitoring Ctr Jilin Prov Changchun Peoples R China Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing Peoples R China

Winter climate conditions have a great effect on spring soil moisture (SM) in cold regions. The frequent occurrence of spring drought events in the Songnen Plain poses a significant threat to food security. In this study, we selected the random forest (RF) algorithm from three currently popular machine learning algorithms to predict the spatial pattern of average SM for the period of April 1-15 and April 16-30 with 1 km resolution in the dry cropland of southern Songnen Plain (SSNP) using 0-10 cm SM data for each April from 81 agro-meteorological stations and winter climate data (e.g., temperature, snowfall), soil properties and topographic relief. Compared to the existing ERA5_Land and SMAP SM products, we obtain higher precision SM data (April 1-15: r = 0.74, RMSE = 0.050 m(3) m(-3);April 16-30: r = 0.73, RMSE = 0.051 m(3) m(-3)). Drought grades were classified based on predicted SM data, and the results indicate agricultural drought was mainly influenced by the change of winter snowfall/snow depth, with western SSNP being more susceptible to drought because of soil properties. Compared with current SM data products, the RF model proposed in this study can implement more accurate prediction of spring soil drought based on winter climate, provide important information for agricultural management departments to prepare for spring cropping and irrigation, and avoid further soil salinization caused by drought.

关键词： cropland machine learning algorithms soil moisture Songnen plain spring agricultural drought

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Experimental Evaluation of Spectrum Handoff Management with machine learning algorithms Using Software Defined Radio

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期138卷 149-170页

作者： Babjan, Patan Rajendran, V. Vels Inst Sci Technol & Adv Studies VISTAS Elect & Commun Engn Chennai India

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by employing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Na & iuml;ve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non-orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyse the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy. The performance of proposed system is compared with the literature and shown a significant improvement that proves the evidence of our findings.

关键词： Spectrum handoff Spectrum sensing Cognitive radio machine learning algorithms Software defined radio NOMA

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Estimation of Wind Turbine Energy Production Value by Using machine learning algorithms and Development of Implementation Program

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第6期43卷 692-704页

作者： Aksoy, Bekir Selbas, Resat Isparta Univ Appl Sci Dept Mechatron Engn TR-32100 Isparta Turkey Isparta Univ Appl Sci Dept Energy Syst Engn Isparta Turkey

In recent years, analyzing the data gathered during production process of the energy has become an important issue in the wind energy sector in order to increase the efficiency of the produced energy. This study estimated the produced energy value by using machine learning algorithms depending on the temperature, wind speed and direction values gathered from the wind turbine in 2015. A mathematical equation which correctly estimates the energy production value by 90% has been obtained. A computer program was developed for other users to see the results of this mathematical equation.

关键词： Wind turbine energy renewable energy machine learning algorithms data analysis

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Analyzing the parallelization and structural impact of machine learning algorithms in social networks: a simulation-based approach

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2024年

作者： Banihashemi, Sepideh Veksler, Keren Abhari, Abdolreza Toronto Metropolitan Univ Dept Comp Sci Toronto ON M5B 2K3 Canada

Analyzing social media networks is crucial for understanding and uncovering common interests and characteristics among users within human societies. In this context, we simulated a simple application of human interaction in social networks, which involves users following others based on text similarity. We then investigated the effects of various machine learning (ML) algorithms employed in the applications to be used as recommendations to decision-making users. A novel agent-based social network simulator called distributed system and multinode processing is developed to assess the parallelization of the ML algorithms (i.e., K-means clustering, cosine similarity, support vector machine, multilayer perceptron) using bag of words (BoW) term frequency-inverse document frequency vectorization by evaluating their performance when executed in parallel across distributed heterogeneous resources. In addition, this simulator compares the effects of BoW with the Doc2Vec model on network structure by observing the differences in detected communities and resulting network graphs when a selected user follows the recommendations produced by an employed algorithm. Three real datasets were used in the experiments: Twitter, Scientific Research Papers, and Retail. This work's contribution is a unique in-house agent-based simulator developed to analyze the impact of common ML algorithms, including supervised and unsupervised learning, on social networks.

关键词： Social network simulation social network analysis machine learning algorithms bag of words (BoW) Doc2Vec

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Malware Detection: A Framework for Reverse Engineered Android Applications Through machine learning algorithms

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IEEE ACCESS 2022年 10卷 89031-89050页

作者： Urooj, Beenish Shah, Munam Ali Maple, Carsten Abbasi, Muhammad Kamran Riasat, Sidra COMSATS Univ Islamabad Dept Comp Sci Islamabad 45550 Pakistan Univ Warwick WMG Coventry CV4 7AL W Midlands England Univ Sindh Dept Distance Continuing & Comp Educ Hyderabad 76080 Pakistan

Today, Android is one of the most used operating systems in smartphone technology. This is the main reason, Android has become the favorite target for hackers and attackers. Malicious codes are being embedded in Android applications in such a sophisticated manner that detecting and identifying an application as a malware has become the toughest job for security providers. In terms of ingenuity and cognition, Android malware has progressed to the point where they're more impervious to conventional detection techniques. Approaches based on machine learning have emerged as a much more effective way to tackle the intricacy and originality of developing Android threats. They function by first identifying current patterns of malware activity and then using this information to distinguish between identified threats and unidentified threats with unknown behavior. This research paper uses Reverse Engineered Android applications' features and machine learning algorithms to find vulnerabilities present in Smartphone applications. Our contribution is twofold. Firstly, we propose a model that incorporates more innovative static feature sets with the largest current datasets of malware samples than conventional methods. Secondly, we have used ensemble learning with machine learning algorithms i.e., AdaBoost, Support Vector machine (SVM), etc. to improve our model's performance. Our experimental results and findings exhibit 96.24% accuracy to detect extracted malware from Android applications, with a 0.3 False Positive Rate (FPR). The proposed model incorporates ignored detrimental features such as permissions, intents, Application Programming Interface (API) calls, and so on, trained by feeding a solitary arbitrary feature, extracted by reverse engineering as an input to the machine.

关键词： Smart phones Malware Feature extraction machine learning algorithms Predictive models machine learning Static analysis Android applications benign feature extraction malware detection reverse engineering machine learning

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Ensembling of Efficient Deep Convolutional Networks and machine learning algorithms for Resource Effective Detection of Tuberculosis Using Thoracic (Chest) Radiography

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IEEE ACCESS 2022年 10卷 85442-85458页

作者： Mehrrotraa, Rajat Ansari, M. A. Agrawal, Rajeev Tripathi, Pragati Bin Heyat, Md Belal Al-Sarem, Mohammed Muaad, Abdullah Yahya Mohammed Nagmeldin, Wamda Abdelrahman Elhag Abdelmaboud, Abdelzahir Saeed, Faisal GL Bajaj Inst Technol & Management Dept Elect & Elect Engn Greater Noida 201306 India Gautam Buddha Univ Dept Elect Engn Sch Engn Greater Noida 201308 India Lloyd Inst Engn & Technol Dept Comp Sci Greater Noida 201306 India Shenzhen Univ IoT Res Ctr Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China Int Inst Informat Technol Ctr VLSI & Embedded Syst Technol Hyderabad 500032 Telangana India Novel Global Community Educ Fdn Dept Sci & Engn Hebersham NSW 2770 Australia Taibah Univ Coll Comp Sci & Engn Medina 42353 Saudi Arabia Saba Reg Univ Dept Comp Sci Marib Yemen Univ Mysore Dept Studies Comp Sci Mysore 570006 Karnataka India Sanaa Community Coll IT Dept Sanaa Yemen Prince Sahara bin Abdulaziz Univ Dept Informat Syst Coll Comp Engn & Sci Al Kharj 16278 Saudi Arabia King Khalid Univ Dept Informat Syst Muhayel Aseer 61913 Saudi Arabia Birmingham City Univ DAAI Res Grp Dept Comp & Data Sci Sch Comp & Digital Technol Birmingham B4 7XG W Midlands England Univ Teknol Malaysia UTM Big Data Ctr Ibnu Sina Inst Sci & Ind Johor Baharu 81310 Malaysia

Tuberculosis (TB) is a communicable pulmonary disorder and countries with low and middle-income share a higher TB burden as compared to others. The year 2020-2021 universally saw a brutal pandemic in the form of COVID-19, that crushed various lives, health infrastructures, programs, and economies worldwide at an unprecedented speed. The gravity of this estimation gets intensified in systems with limited technological advancements. To assist in the identification of tuberculosis, we propose the ensembling of efficient deep convolutional networks and machine learning algorithms that do not entail heavy computational resources. In this paper, the three of the most efficient deep convolutional networks and machine learning algorithms are employed for resource-effective (low computational and basic Imaging requirements) detection of Tuberculosis. The pivotal features extracted from the deep networks are ensembled and subsequently, the machine learning algorithms are used to identify the images based on the extracted features. The said model underwent k-fold cross-validation and achieved an accuracy of 87.90% and 99.10% with an AUC of 0.94 and 1 respectively in identifying TB infected images from Normal and COVID infected images. Also, the model's error rate, F-score, and youden's index values of 0.0093, 0.9901, and 0.9812 for TB versus COVID identification along with the model's accuracy claim that its use can be beneficial in identifying TB infections amid this COVID-19 pandemic, predominantly in countries with limited resources.

关键词： Feature extraction Lung COVID-19 machine learning algorithms Diseases Radiography X-ray imaging COVID-19 chest X-ray deep convolutional networks ensemble learning machine learning tuberculosis

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