Seismic activity prediction of the northern part of Pakistan from novel machine learning technique

Aslam, Bilal; Zafar, Adeel; Khalil, Umer; Azam, Umar

doi:10.1007/s10950-021-09982-3

Cited by 11 publications

(5 citation statements)

References 56 publications

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“…This study implements the BRT, SVM, and MDA procedures through the SDM package in the R platform. To calibrate and test the models, the datasets are further divided randomly into the proportion of 70% (datasets utilized for calibration of the models) and 30% (datasets utilized for testing of the model), based on the literature [15,51,[81][82][83][84]. The Receiver Operating Characteristic (ROC) curve technique and Area Under Curve (AUC) technique are utilized for the performance validation of the models [85][86][87][88].…”

Section: Implementation and Accuracy Assessmentmentioning

confidence: 99%

Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

et al. 2021

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Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks.

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Section: Implementation and Accuracy Assessmentmentioning

confidence: 99%

Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

et al. 2021

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“…The earthquake magnitude deficit is defined as the difference between the maximum observed magnitude and the maximum possible magnitude defined by ai/bi from the Gutenberg-Richter relationship, and is formulated as shown in Equation (14). Note that a i and b i are the parameters of the Gutenberg-Richter relationship, and Max represents…”

Section: Earthquake Magnitude Deficitmentioning

confidence: 99%

“…The authors claimed to achieve 65% accuracy. A seismic classification system was presented by Aslam et al [14], using machine learning algorithms, while attaining a 79% accuracy score. Table 8 evidently highlights that the proposed model performed better, with a 95.13% accuracy score against the extant approaches to the Hindukush region, on the basis of accuracy as well as precision, recall, and F1-score.…”

Section: Hindukushmentioning

confidence: 99%

“…The authors used an artificial neural network for earthquake prediction, and reported accuracy of 80%. A different approach was used by Aslam et al [14] in the northern areas of Pakistan, for the prediction of seismic activity. The authors implemented the support vector machine and hybrid neural network on the targeted area, to predict earthquake occurrence for a period of one month: the maximum accuracy of their models was 79% on one dataset.…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Deep Learning Approach to Seismic Activity Prediction

et al. 2023

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Seismic activity prediction has been a challenging research domain: in this regard, accurate prediction using historical data is an intricate task. Numerous machine learning and traditional approaches have been presented lately for seismic activity prediction; however, no generalizable model exists. In this work, we consider seismic activity predication as a binary classification problem, and propose a deep neural network architecture for the classification problem, using historical data from Chile, Hindukush, and Southern California. After obtaining the data for the three regions, a data cleaning process was used, which was followed by a feature engineering step, to create multiple new features based on various seismic laws. Afterwards, the proposed model was trained on the data, for improved prediction of the seismic activity. The performance of the proposed model was evaluated and compared with extant techniques, such as random forest, support vector machine, and logistic regression. The proposed model achieved accuracy scores of 98.28%, 95.13%, and 99.29% on the Chile, Hindukush, and Southern California datasets, respectively, which were higher than the current benchmark model and classifiers. In addition, we also conducted out-sample testing, where the evaluation metrics confirmed the generality of our proposed approach.

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“…Artificial intelligence is the branch of science that has been used to develop intelligent machines. Artificial intelligence has emerged with many distinct approaches including expert systems (ES), machine learning (ML), deep learning (DL), neural networks (NN) and other techniques that have been applied for earthquake prediction ( Lim et al, 2020 ; Yousefzadeh, Hosseini & Farnaghi, 2021 ; Aslam et al, 2021a , 2021b ). Mirrashid (2014) applied Adapted Neuro Fuzzy inference system (ANFIS) to predict magnitudes of expected seismic events using Fuzzy C-means algorithm.…”

Section: Related Workmentioning

confidence: 99%

A framework for the prediction of earthquake using federated learning

Tehseen

Farooq

Abid

2021

PeerJ Computer Science

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Earthquakes are a natural phenomenon which may cause significant loss of life and infrastructure. Researchers have applied multiple artificial intelligence based techniques to predict earthquakes, but high accuracies could not be achieved due to the huge size of multidimensional data, communication delays, transmission latency, limited processing capacity and data privacy issues. Federated learning (FL) is a machine learning (ML) technique that provides an opportunity to collect and process data onsite without compromising on data privacy and preventing data transmission to the central server. The federated concept of obtaining a global data model by aggregation of local data models inherently ensures data security, data privacy, and data heterogeneity. In this article, a novel earthquake prediction framework using FL has been proposed. The proposed FL framework has given better performance over already developed ML based earthquake predicting models in terms of efficiency, reliability, and precision. We have analyzed three different local datasets to generate multiple ML based local data models. These local data models have been aggregated to generate global data model on the central FL server using FedQuake algorithm. Meta classifier has been trained at the FL server on global data model to generate more accurate earthquake predictions. We have tested the proposed framework by analyzing multidimensional seismic data within 100 km radial area from 34.708° N, 72.5478° E in Western Himalayas. The results of the proposed framework have been validated against instrumentally recorded regional seismic data of last thirty-five years, and 88.87% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of earthquake early warning systems.

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Seismic activity prediction of the northern part of Pakistan from novel machine learning technique

Cited by 11 publications

References 56 publications

Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

A Generalized Deep Learning Approach to Seismic Activity Prediction

A framework for the prediction of earthquake using federated learning

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