A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources

Chen, Huazhou; Chen, An; Xu, Lili; Xie, Hai; Qiao, Hanli; Lin, Qinyong; Cai, Ken

doi:10.1016/j.agwat.2020.106303

Cited by 248 publications

(100 citation statements)

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“…Prime ML applications in SHM and JSM include structure damage detection [18] and on-site worker safety monitoring [19]. The rapid evolution of graphics processing units (GPUs) has dramatically improved the computational capacity for processing ML algorithms, which has led to the advent of an increasing amount of deep learning (DL) applications that are underpinned by improved GPU performance [20]. In particular, the convolutional neural network (CNN), a DL algorithm, achieved extraordinary results in the ImageNET Large Scale Visual Recognition Challenge 2012 (ILSVRC2012), which is a benchmark in object classification and detection for thousands of object classes and millions of images [21].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the convolutional neural network (CNN), a DL algorithm, achieved extraordinary results in the ImageNET Large Scale Visual Recognition Challenge 2012 (ILSVRC2012), which is a benchmark in object classification and detection for thousands of object classes and millions of images [21]. Currently, DL has outperformed many advanced algorithms in numerous fields [20,22,23]. More and more, DL applications are being developed and deployed to address image classification, data augmentation and object detection problems [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

et al. 2021

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Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

et al. 2021

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“…In recent years, new techniques and models have been developed by researchers worldwide [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. During the last 6 years, several studies have been individualized regarding the flash-flood susceptibility investigations, which were carried out through the integration of GIS techniques with bivariate statistical models such as: frequency ratio [ 36 ], weights of evidence [ 37 ], statistical index [ 38 ], evidential belief function [ 39 ], certainty factor [ 40 ], or index of entropy [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Costache

Arabameri

Blaschke

et al. 2021

Sensors

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There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

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“…It is well established that the cost and use of energy affect human lives every day. In this sense, many issues arise from the content of energy consumption such as acid rain, dependency on depleting supplies of fossil fuels, greenhouse gas emissions [8][9][10][11][12][13][14][15][16][17], climate change [18][19][20][21], as well as environmental concerns that come along with energy power supply [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In recent years, various techniques have been used for the optimal design of the HVAC system [36][37][38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

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Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

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A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources

Cited by 248 publications

References 42 publications

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

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