Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems

Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process:Aceh province,Indonesia

Catastrophic natural hazards,such as earthquake,pose serious threats to properties and human lives in urban areas.Therefore,earthquake risk assessment(ERA)is indispensable in disaster management.ERA is an integration of the extent of probability and vulnerability of assets.This study develops an integrated model by using the artificial neural network–analytic hierarchy process(ANN–AHP)model for constructing the ERA map.The aim of the study is to quantify urban population risk that may be caused by impending earthquakes.The model is applied to the city of Banda Aceh in Indonesia,a seismically active zone of Aceh province frequently affected by devastating earthquakes.ANN is used for probability mapping,whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering.The risk map is subsequently created by combining the probability,hazard,and vulnerability maps.Then,the risk levels of various zones are obtained.The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%.Furthermore,results show that the central and southeastern regions of the city have moderate to very high risk classifications,whereas the other parts of the city fall under low to very low earthquake risk classifications.The findings of this research are useful for government agencies and decision makers,particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.

APG: A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are generated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker performances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7%from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4%from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental binary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.

SWPT:An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors

The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources.Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner.Hence,this study aimed at developing a user-friendly geographic information system(GIS)tool,Sub-Watershed Prioritization Tool(SWPT),using the Python programming language to decrease any possible uncertainty.It used geospatial-statistical techniques for analyzing morphometric and topohydrological factors and automatically identifying critical and priority sub-watersheds.In order to assess the capability and reliability of the SWPT tool,it was successfully applied in a watershed in the Golestan Province,Northern Iran.Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds.It provided a cost-effective approach for prioritization of sub-watersheds.Therefore,the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed.

Debris flow simulation 2D(DFS 2D):Numerical modelling of debris flows and calibration of friction parameters

Debris flows are rapid mass movements with a mixture of rock,soil and water.High-intensity rainfall events have triggered multiple debris flows around the globe,making it an important concern from the disaster management perspective.This study presents a numerical model called debris flow simulation 2D(DFS 2D)and applicability of the proposed model is investigated through the values of the model parameters used for the reproduction of an occurred debris flow at Yindongzi gully in China on 13 August 2010.The model can be used to simulate debris flows using three different rheologies and has a userfriendly interface for providing the inputs.Using DFS 2D,flow parameters can be estimated with respect to space and time.The values of the flow resistance parameters of model,dry-Coulomb and turbulent friction,were calibrated through the back analysis and the values obtained are 0.1 and 1000 m/s^(2),respectively.Two new methods of calibration are proposed in this study,considering the crosssectional area of flow and topographical changes induced by the debris flow.The proposed methods of calibration provide an effective solution to the cumulative errors induced by coarse-resolution digital elevation models(DEMs)in numerical modelling of debris flows.The statistical indices such as Willmott's index of agreement,mean-absolute-error,and normalized-root-mean-square-error of the calibrated model are 0.5,1.02 and 1.44,respectively.The comparison between simulated and observed values of topographic changes indicates that DFS 2D provides satisfactory results and can be used for dynamic modelling of debris flows.

Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print attacks.The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness.Seven assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and ensembles.The predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and ACER.Three standard datasets were used in the investigation.The main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature vector.The fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%accuracy.Additionally,conduct an extensive experiment on cross datasets for detailed analysis.The results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.

Comparison of machine learning models for gully erosion susceptibility mapping

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.

Prediction of spatial soil loss impacted by long-term land-use/land-cover change in a tropical watershed

The devastating effect of soil erosion is one of the major sources of land degradation that affects human lives in many ways which occur mainly due to deforestation, poor agricultural practices, overgrazing,wildfire and urbanization. Soil erosion often leads to soil truncation, loss of fertility, slope instability, etc.which causes irreversible effects on the poorly renewable soil resource. In view of this, a study was conducted in Kelantan River basin to predict soil loss as influenced by long-term land use/land-cover(LULC) changes in the area. The study was conducted with the aim of predicting and assessing soil erosion as it is influenced by long-term LULC changes. The 13,100 km^2 watershed was delineated into four sub-catchments Galas, Pergau, Lebir and Nenggiri for precise result estimation and ease of execution. GIS-based Universal Soil Loss Equation(USLE) model was used to predict soil loss in this study. The model inputs used for the temporal and spatial calculation of soil erosion include rainfall erosivity factor,topographic factor, land cover and management factor as well as erodibility factor. The results showed that 67.54% of soil loss is located under low erosion potential(reversible soil loss) or 0-1 t ha^(-1) yr^(-1) soil loss in Galas, 59.17% in Pergau, 53.32% in Lebir and 56.76% in Nenggiri all under the 2013 LULC condition.Results from the correlation of soil erosion rates with LULC changes indicated that cleared land in all the four catchments and under all LULC conditions(1984-2013) appears to be the dominant with the highest erosion losses. Similarly, grassland and forest were also observed to regulate erosion rates in the area. This is because the vegetation cover provided by these LULC types protects the soil from direct impact of rain drops which invariably reduce soil loss to the barest minimum. Overall, it was concluded that the results have shown the significance of LULC in the control of erosion. Maps generated from the study may be useful to planners and land use managers to take appropriate decisions for soil conservation.

Assessment of geochemical and sedimentological characteristics of atmospheric dust in Shiraz,southwest Iran

Geogenic dust is commonly believed to be one of the most important environmental problems in the Middle East.The present study investigated the geochemical characteristics of atmospheric dust particles in Shiraz City(south of Iran).Atmospheric dust samples were collected through a dry collector method by using glass trays at 10 location sites in May 2018.Elemental composition was analysed through inductively coupled plasma optical emission spectrometry.Meteorological data showed that the dustiest days were usually in spring and summer,particularly in April.X-ray diffraction analysis of atmospheric dust samples indicated that the mineralogical composition of atmospheric dust was calcite+dolomite(24%)>palygorskite(18%)>quartz(14%)>muscovite(13%)>albite(11%)>kaolinite(7%)>gypsum(7%)>zircon=anatase(3%).The high occurrence of palygorskite(16%-23%) could serve as a tracer of the source areas of dust storms from the desert of Iraq and Saudi Arabia to the South of Iran.Scanning electron microscopy indicated that the sizes of the collected dust varied from 50 μm to0.8 μm,but 10 μm was the predominant size.The atmospheric dust collected had prismatic trigonal-rhombohedral crystals and semi-rounded irregular shapes.Moreover,diatoms were detected in several samples,suggesting that emissions from dry-bed lakes,such as Hoor Al-Azim Wetland(located in the southwest of Iran),also contributed to the dust load.Backward trajectory simulations were performed at the date of sampling by using the NOAA HYSPLIT model.Results showed that the sources of atmospheric dust in the study area were the eastern area of Iraq,eastern desert of Saudi Arabia,Kuwait and Khuzestan Province.The Ca/Al ratio of the collected samples(1.14) was different from the upper continental crust(UCC) value(UCC=0.37),whereas Mg/A1(0.29),K/Al(0.22) and Ti/Al(0.07) ratios were close to the UCC value(0.04).This condition favours desert calcisols as the main mineral dust sources.Analysis of the crustal enrichment factor(EF_(crustal)) revealed geogenic sources for V,Mo,Pb,Sr,Cu and Zn(<2),whereas anthropogenic sources affected As,Cd,Cr and Ni.

Energy: Preface

Energy is a vital commodity that sustains human lives,as well as economic processes.The challenges towards energy generation,demand and supply are plenty owing to the use of fossil fuels leading to climate change and environmental problems like water and air pollution.With the increasing awareness over climate change,post Paris Agreement,the role of energy plays a key role towards achieving the proposed target.The contributions in this Special Issue of Geoscience Frontiers on Energy includes 8 papers from esteemed research groups worldwide which explores,highlights and provide new insights towards the various aspects of energy.

Natural Hazards and Social Vulnerability of Place: The Strength-Based Approach Applied to Wollongong, Australia

Natural hazards pose significant threats to different communities and various places around the world.Failing to identify and support the most vulnerable communities is a recipe for disaster. Many studies have proposed social vulnerability indices for measuring both the sensitivity of a population to natural hazards and its ability to respond and recover from them. Existing techniques,however, have not accounted for the unique strengths that exist within different communities to help minimize disaster loss. This study proposes a more balanced approach referred to as the strength-based social vulnerability index(SSVI). The proposed SSVI technique, which is built on sound sociopsychological theories of how people act during disasters and emergencies, is applied to assess comparatively the social vulnerability of different suburbs in the Wollongong area of New South Wales, Australia. The results highlight suburbs that are highly vulnerable, and demonstrates the usefulness of the technique in improving understanding of hotspots where limited resources should be judiciously allocated to help communities improve preparedness, response, and recovery from natural hazards.

Spatial flood susceptibility mapping using an explainable artificial intelligence(XAI)model

Floods are natural hazards that lead to devastating financial losses and large displacements of people.Flood susceptibility maps can improve mitigation measures according to the specific conditions of a study area.The design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning models.Although these models have achieved better accuracy than traditional models,they are not widely used by stakeholders due to their black-box nature.In this study,we propose the application of an explainable artificial intelligence(XAI)model that incorporates the Shapley additive explanation(SHAP)model to interpret the outcomes of convolutional neural network(CNN)deep learning models,and analyze the impact of variables on flood susceptibility mapping.This study was conducted in Jinju Province,South Korea,which has a long history of flood events.Model performance was evaluated using the area under the receiver operating characteristic curve(AUROC),which showed a prediction accuracy of 88.4%.SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study area.In light of these findings,we recommend the use of XAIbased models in future flood susceptibility mapping studies to improve interpretations of model outcomes,and build trust among stakeholders during the flood-related decision-making process.

Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques

Australia is a relatively stable continental region but not tectonically inert,having geological conditions that are susceptible to liquefaction when subjected to earthquake ground motion.Liquefaction hazard assessment for Australia was conducted because no Australian liquefaction maps that are based on modern Al techniques are currently available.In this study,several conditioning factors including Shear wave velocity(Vs30),clay content,soil water content,soil bulk density,soil thickness,soil pH,distance from river,slope and elevation were considered to estimate the liquefaction potential index(LPI).By considering the Probabilistic Seismic Hazard Assessment(PSHA)technique,peak ground acceleration(PGA)was derived for 50 yrs period(500 and 2500 yrs return period)in Australia.Firstly,liquefaction hazard index(LHI)(effects based on the size and depth of the liquefiable areas)was estimated by considering the LPI along with the 2%and 10%exceedance probability of earthquake hazard.Secondly,ground acceleration data from the Geoscience Australia projecting 2%and 10%exceedance rate of PGA for 50 yrs were used in this study to produce earthquake induced soil liquefaction hazard maps.Thirdly,deep neural net-works(DNNs)were also exerted to estimate liquefaction hazard that can be reported as liquefaction hazard base maps for Australia with an accuracy of 94%and 93%,respectively.As per the results,very-high liquefaction hazard can be observed in Western and Southern Australia including some parts of Victoria.This research is the first ever country-scale study to be considered for soil liquefaction hazard in Australia using geospatial information in association with PSHA and deep learning techniques.This study used an earthquake design magnitude threshold of Mw 6 using the source model characterization.The resulting maps present the earthquake-triggered liquefaction hazard and are intending to establish a conceptual structure to guide more detailed investigations as may be required in the future.The limitations of deep learning models are complex and require huge data,knowledge on topology,parameters,and training method whereas PSHA follows few assumptions.The advantages deal with the reusability of model codes and its transferability to other similar study areas.This research aims to support stakeholders'on decision making for infrastructure investment,emergency planning and prioritisation of post-earthquake reconstruction projects.