Physics and Chemistry of the Earth最新文献

Artificial neural networks for estimating historical daily missing evaporation to support sustainable development in Saudi Arabia

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-18 DOI: 10.1016/j.pce.2025.103949

Samyah Salem Refadah , Mohd Yawar Ali Khan

Evaporation (EVAP) is a crucial component of the water cycle; however, its estimation is challenging due to its complexity and numerous influencing factors. Estimating EVAP is essential for identifying the environmental effects of heavy metals. It enables the forecasting of contamination risks, concentration changes, and the establishment of suitable mitigation suggestions, particularly in semi-arid and arid regions. This research presents a novel approach to evaluating the efficacy of regional models in estimating missing EVAP at a gauging location in the Al-Medina region. The estimates were generated using time series data on wind speed (WS), relative humidity (RH), temperature (TEMP), and evaporation (EVAP) from January to December (1974–1977; 2007–2009) through models employing the artificial neural network (ANN) feedforward backpropagation (FFBP) technique. The initial phase involved the development and training of the ANN, utilizing the FFBP technique in MATLAB (Version R2015a). The optimal network was then used to predict the EVAP values for 1974–1976, a missing parameter at the gauging site, by employing TEMP, RH, WS, and EVAP data from 2007 to 2009. The second stage includes verifying the predicted EVAP values (1974–1976) by using them to estimate the EVAP values for 1977 at gauged sites. Four ANNs (T1-T4) with distinct configurations were built and trained using the FFBP algorithm. The model's predicted values are compared with the actual EVAP values observed at measurement sites. The value of R² for the optimal topology was determined to be 0.981, with a mean squared error (MSE) of 0.019.

{"title":"Artificial neural networks for estimating historical daily missing evaporation to support sustainable development in Saudi Arabia","authors":"Samyah Salem Refadah , Mohd Yawar Ali Khan","doi":"10.1016/j.pce.2025.103949","DOIUrl":"10.1016/j.pce.2025.103949","url":null,"abstract":"<div><div>Evaporation (EVAP) is a crucial component of the water cycle; however, its estimation is challenging due to its complexity and numerous influencing factors. Estimating EVAP is essential for identifying the environmental effects of heavy metals. It enables the forecasting of contamination risks, concentration changes, and the establishment of suitable mitigation suggestions, particularly in semi-arid and arid regions. This research presents a novel approach to evaluating the efficacy of regional models in estimating missing EVAP at a gauging location in the Al-Medina region. The estimates were generated using time series data on wind speed (WS), relative humidity (RH), temperature (TEMP), and evaporation (EVAP) from January to December (1974–1977; 2007–2009) through models employing the artificial neural network (ANN) feedforward backpropagation (FFBP) technique. The initial phase involved the development and training of the ANN, utilizing the FFBP technique in MATLAB (Version R2015a). The optimal network was then used to predict the EVAP values for 1974–1976, a missing parameter at the gauging site, by employing TEMP, RH, WS, and EVAP data from 2007 to 2009. The second stage includes verifying the predicted EVAP values (1974–1976) by using them to estimate the EVAP values for 1977 at gauged sites. Four ANNs (T1-T4) with distinct configurations were built and trained using the FFBP algorithm. The model's predicted values are compared with the actual EVAP values observed at measurement sites. The value of R<sup>2</sup> for the optimal topology was determined to be 0.981, with a mean squared error (MSE) of 0.019.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103949"},"PeriodicalIF":3.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variations in the compositional profile of polycyclic aromatic moieties in coals from Bara, Patala and Salt Range formations: Insights into the mechanisms affecting the sedimentary record

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-18 DOI: 10.1016/j.pce.2025.103950

Arif Nazir , Qaiser Hussain , Saeed Abbas , Fraz Khalid , Muhammad Deen , Abid Ali , Munawar Iqbal

This study focuses on the variations in concentration and composition of polycyclic aromatic hydrocarbons (PAHs) in different sedimentary records from Baluchistan, Sindh, and Punjab. Proximate and ultimate analyses, especially calorific values characterized Padhrar coal as bituminous A, Duki coal as highly volatile bituminous C, and sub-bituminous C class for Lakhra coal. The geochemical profile of organic matter was determined using substituted naphthalenes, phenanthrenes, and dibenzothiophenes ratios. Coal samples were thermally mature and indicated shale type lithology as evidenced by geochemical criteria. Moreover, the presence of depositional environmental biomarkers like benzothiophene and alkyl benzothiophene indicated the carbonate evaporate depositional environment, whilst the presence of 1-methylphenanthrene, pyrene, benzopyrene, and perylene in the aromatic fraction revealed the dominance of terrestrial organic matter.

{"title":"Variations in the compositional profile of polycyclic aromatic moieties in coals from Bara, Patala and Salt Range formations: Insights into the mechanisms affecting the sedimentary record","authors":"Arif Nazir , Qaiser Hussain , Saeed Abbas , Fraz Khalid , Muhammad Deen , Abid Ali , Munawar Iqbal","doi":"10.1016/j.pce.2025.103950","DOIUrl":"10.1016/j.pce.2025.103950","url":null,"abstract":"<div><div>This study focuses on the variations in concentration and composition of polycyclic aromatic hydrocarbons (PAHs) in different sedimentary records from Baluchistan, Sindh, and Punjab. Proximate and ultimate analyses, especially calorific values characterized Padhrar coal as bituminous A, Duki coal as highly volatile bituminous C, and sub-bituminous C class for Lakhra coal. The geochemical profile of organic matter was determined using substituted naphthalenes, phenanthrenes, and dibenzothiophenes ratios. Coal samples were thermally mature and indicated shale type lithology as evidenced by geochemical criteria. Moreover, the presence of depositional environmental biomarkers like benzothiophene and alkyl benzothiophene indicated the carbonate evaporate depositional environment, whilst the presence of 1-methylphenanthrene, pyrene, benzopyrene, and perylene in the aromatic fraction revealed the dominance of terrestrial organic matter.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103950"},"PeriodicalIF":3.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Drought susceptibility prediction using novel hybridized methods based on reliability of drought and non-drought samples

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-15 DOI: 10.1016/j.pce.2025.103940

Ran Zhou , Alireza Arabameri , M. Santosh , Abu Reza Md Towfiqul Islam , Most Mastura Munia Farjana Jion

Agricultural droughts are a periodic phenomenon in many regions of the word. Reducing damages to crops from drought events requires increasing the precision of agricultural drought mapping and making forecasts. However, there are very few studies that employ cutting-edge ensemble learning methods to estimate the risk of agricultural drought. In order to strengthen predictions of agricultural drought risk in a systematically explicit manner, we propose and evaluate ensemble models grounded on the credal decision tree (CDT) with Decorate (CDT-Decorate) supervised learning approaches as a case study in Iran. In the Esfahan Province of Iran, a thorough evaluation of the possibility of agricultural drought was conducted. This assessment coupled the five risk components—vulnerability, hazard, exposure, and mitigation—with the parameters that were suitable. Eighteen drought conditioning factors were identified and used to build both the training and validation datasets. A number of evaluation measures that showed the ensemble model's capacity to explain the underlying spatial pattern of agricultural drought events within the research area and forecast the likelihood of future drought phenomena that were used to validate the models. Area Under the Receiver Operating Characteristic Curve (AUCROC) showed that the ensemble CDT-Decorate model was better than the CDT model (AUCROC = 0.755) because it had an AUCROC value of 0.962. When assessing the risk of agricultural drought, the most important elements are population density, land usage, land cover, and distance to the river. The center region, with its intermediate risk (17 %), has a significant disruption of human agricultural activities; the southern region, with its very high risk (16 %), should receive the greatest attention due to its high susceptibility, significant hazardousness, and limited mitigation capability. An analysis of the models' e performances revealed that the ensemble model offered a trustworthy assessment of the risks associated with agricultural drought, and the risk maps it produced are suitable for drought mitigation techniques in the agricultural sector and could be applied in other drought-prone areas.

{"title":"Drought susceptibility prediction using novel hybridized methods based on reliability of drought and non-drought samples","authors":"Ran Zhou , Alireza Arabameri , M. Santosh , Abu Reza Md Towfiqul Islam , Most Mastura Munia Farjana Jion","doi":"10.1016/j.pce.2025.103940","DOIUrl":"10.1016/j.pce.2025.103940","url":null,"abstract":"<div><div>Agricultural droughts are a periodic phenomenon in many regions of the word. Reducing damages to crops from drought events requires increasing the precision of agricultural drought mapping and making forecasts. However, there are very few studies that employ cutting-edge ensemble learning methods to estimate the risk of agricultural drought. In order to strengthen predictions of agricultural drought risk in a systematically explicit manner, we propose and evaluate ensemble models grounded on the credal decision tree (CDT) with Decorate (CDT-Decorate) supervised learning approaches as a case study in Iran. In the Esfahan Province of Iran, a thorough evaluation of the possibility of agricultural drought was conducted. This assessment coupled the five risk components—vulnerability, hazard, exposure, and mitigation—with the parameters that were suitable. Eighteen drought conditioning factors were identified and used to build both the training and validation datasets. A number of evaluation measures that showed the ensemble model's capacity to explain the underlying spatial pattern of agricultural drought events within the research area and forecast the likelihood of future drought phenomena that were used to validate the models. Area Under the Receiver Operating Characteristic Curve (AUCROC) showed that the ensemble CDT-Decorate model was better than the CDT model (AUCROC = 0.755) because it had an AUCROC value of 0.962. When assessing the risk of agricultural drought, the most important elements are population density, land usage, land cover, and distance to the river. The center region, with its intermediate risk (17 %), has a significant disruption of human agricultural activities; the southern region, with its very high risk (16 %), should receive the greatest attention due to its high susceptibility, significant hazardousness, and limited mitigation capability. An analysis of the models' e performances revealed that the ensemble model offered a trustworthy assessment of the risks associated with agricultural drought, and the risk maps it produced are suitable for drought mitigation techniques in the agricultural sector and could be applied in other drought-prone areas.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103940"},"PeriodicalIF":3.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Microplastics in Indian freshwater systems: Multidisciplinary analysis of sources, consequences, and mitigation strategies

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-12 DOI: 10.1016/j.pce.2025.103942

Lone Rafiya Majeed , Lone Fawad Majeed , Deeplata Sharma , Pradeep Verma , Vineet Kumar

Microplastic (MP) pollution represents an escalating environmental hazard, especially in India's freshwater ecosystems, where intensified industrial activities, urbanization, and insufficient waste management have exacerbated contamination. Freshwater ecosystems in India, encompassing rivers, lakes, and reservoirs, are particularly susceptible owing to their significance in sustaining biodiversity, providing potable water, and facilitating agriculture. This study examines the extent, origins, and effects of MP pollution in Indian freshwater systems, emphasising essential research areas to mitigate the associated ecological and public health hazards. This study initially examines the present condition of MP contamination in Indian freshwater systems, pinpointing key sources, including urban wastewater, industrial effluents, and agricultural runoff. This article, subsequently analyse current studies regarding the effects of these pollutants on aquatic ecosystems and underscores critical knowledge deficiencies, especially in evaluating MP toxicity and its interactions with other contaminants. Emphasising the necessity for a multidisciplinary approach, we propose targeted research directions: enhancing monitoring methodologies, developing cost-effective remediation strategies, and examining the socioeconomic factors that contribute to the persistence of pollution. The significance of policy interventions and public awareness campaigns in bolstering scientific efforts to alleviate microplastic pollution has been underscored. This thorough analysis provides actionable insights for researchers, policymakers, and environmental stakeholders seeking sustainable solutions to restore the health and resilience of Indian freshwater ecosystems.

{"title":"Microplastics in Indian freshwater systems: Multidisciplinary analysis of sources, consequences, and mitigation strategies","authors":"Lone Rafiya Majeed , Lone Fawad Majeed , Deeplata Sharma , Pradeep Verma , Vineet Kumar","doi":"10.1016/j.pce.2025.103942","DOIUrl":"10.1016/j.pce.2025.103942","url":null,"abstract":"<div><div>Microplastic (MP) pollution represents an escalating environmental hazard, especially in India's freshwater ecosystems, where intensified industrial activities, urbanization, and insufficient waste management have exacerbated contamination. Freshwater ecosystems in India, encompassing rivers, lakes, and reservoirs, are particularly susceptible owing to their significance in sustaining biodiversity, providing potable water, and facilitating agriculture. This study examines the extent, origins, and effects of MP pollution in Indian freshwater systems, emphasising essential research areas to mitigate the associated ecological and public health hazards. This study initially examines the present condition of MP contamination in Indian freshwater systems, pinpointing key sources, including urban wastewater, industrial effluents, and agricultural runoff. This article, subsequently analyse current studies regarding the effects of these pollutants on aquatic ecosystems and underscores critical knowledge deficiencies, especially in evaluating MP toxicity and its interactions with other contaminants. Emphasising the necessity for a multidisciplinary approach, we propose targeted research directions: enhancing monitoring methodologies, developing cost-effective remediation strategies, and examining the socioeconomic factors that contribute to the persistence of pollution. The significance of policy interventions and public awareness campaigns in bolstering scientific efforts to alleviate microplastic pollution has been underscored. This thorough analysis provides actionable insights for researchers, policymakers, and environmental stakeholders seeking sustainable solutions to restore the health and resilience of Indian freshwater ecosystems.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103942"},"PeriodicalIF":3.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Theoretical basis and analyses of temperature responses of water-saturated rocks to rapid changes in confining pressure

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-11 DOI: 10.1016/j.pce.2025.103925

Xiaoqiu Yang , Alexander H.D. Cheng , Weiren Lin , Hehua Xu , Huai Zhang

The temperature response of water-saturated rocks to stress changes is critical for understanding thermal anomalies in the crust, because most porous rocks in the shallow crust are saturated with water. Based on the adiabatic effective stress law and porothermoelasticity theory, we derived the adiabatic pressure derivative of temperature (β = (∂T/∂P)_s) of water-saturated rock (β_wet) in terms of that of dry rock (β_dry) and water (β_dry), and other measurable physical parameters. Then, we calculated the ranges of β_wet for 15 representative water-saturated rocks at background temperature (T₀) between 23 and 50°C. The results showed that β_wet (1.58–10.79 mK/MPa) was greater than β_dry (1.52–6.15 mK/MPa) for all rocks. The ratio of β_wet to β_dry is more significant for rocks with higher compressibility. For instance, for rocks with drained bulk modulus no more than 10 GPa, β_wet at 50°C (10.71 mK/MPa for Berea sandstone) can be as much as twice of β_dry at 23°C (5.86 mK/MPa). Also, β_wet was observed to linearly increase with the increase of T₀. The theory allows us to gain understanding on the coseismic temperature responses, such as the temperature anomalies documented in boreholes drilled through seismically ruptured active faults after the Chi-Chi, Wenchuan, and Tohoku earthquakes.

{"title":"Theoretical basis and analyses of temperature responses of water-saturated rocks to rapid changes in confining pressure","authors":"Xiaoqiu Yang , Alexander H.D. Cheng , Weiren Lin , Hehua Xu , Huai Zhang","doi":"10.1016/j.pce.2025.103925","DOIUrl":"10.1016/j.pce.2025.103925","url":null,"abstract":"<div><div>The temperature response of water-saturated rocks to stress changes is critical for understanding thermal anomalies in the crust, because most porous rocks in the shallow crust are saturated with water. Based on the adiabatic effective stress law and porothermoelasticity theory, we derived the adiabatic pressure derivative of temperature (<em>β</em> = (∂<em>T</em>/∂<em>P</em>)<sub><em>s</em></sub>) of water-saturated rock (<em>β</em><sub>wet</sub>) in terms of that of dry rock (<em>β</em><sub>dry</sub>) and water (<em>β</em><sub>dry</sub>), and other measurable physical parameters. Then, we calculated the ranges of <em>β</em><sub>wet</sub> for 15 representative water-saturated rocks at background temperature (<em>T</em><sub>0</sub>) between 23 and 50°C. The results showed that <em>β</em><sub>wet</sub> (1.58–10.79 mK/MPa) was greater than <em>β</em><sub>dry</sub> (1.52–6.15 mK/MPa) for all rocks. The ratio of <em>β</em><sub>wet</sub> to <em>β</em><sub>dry</sub> is more significant for rocks with higher compressibility. For instance, for rocks with drained bulk modulus no more than 10 GPa, <em>β</em><sub>wet</sub> at 50°C (10.71 mK/MPa for Berea sandstone) can be as much as twice of <em>β</em><sub>dry</sub> at 23°C (5.86 mK/MPa). Also, <em>β</em><sub>wet</sub> was observed to linearly increase with the increase of <em>T</em><sub>0</sub>. The theory allows us to gain understanding on the coseismic temperature responses, such as the temperature anomalies documented in boreholes drilled through seismically ruptured active faults after the Chi-Chi, Wenchuan, and Tohoku earthquakes.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103925"},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating the optimal replacement percentage of various types of coal waste with chemical additives in concrete construction for sustainable energy applications 研究在混凝土建筑中使用化学添加剂替代各类煤炭废弃物以实现可持续能源应用的最佳比例

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-11 DOI: 10.1016/j.pce.2025.103933

Mahdi Shariati , Mehdi Tazikeh , Morteza Naghipour , Bagher Hoseinian , Hesam Kamyab , Ali Toghroli , Majid Khorami

The large coal production and consumption has caused environmental problems worldwide as a source of energy production with irreparable effects on soil, water, and the ecosystem. In addition, producing coal waste in coal washing plants and burying it intensifies the issue in nature. Due to the rising generation of coal waste from various sources, this study utilized several forms of coal waste obtained from a coal-washing plant in the production of both structural concrete (with a water-cement ratio of 0.54) and non-structural concrete (with a water-cement ratio of 0.7). The impact of coal waste on compressive strength (CS) was examined at curing ages of 7, 28, and 56 days. Various percentages of coal waste were substituted for both cement and sand. A superplasticizer was incorporated into the concrete mixtures to enhance the workability and achieve the desired slump and strength levels. According to the compressive strength findings, the ideal replacement level of sand with jig coal waste was 30 %. For 56-day-old specimens, the optimal substitution rates for cement with jig coal waste powder, flotation coal waste, and coal waste ash were found to be 10 %, 10 %, and 20 %, respectively. Notably, adding 10 % coal waste powder and coal waste ash increased compressive strength by 22 %, 23 %, and 44 % at 56 days.

{"title":"Investigating the optimal replacement percentage of various types of coal waste with chemical additives in concrete construction for sustainable energy applications","authors":"Mahdi Shariati , Mehdi Tazikeh , Morteza Naghipour , Bagher Hoseinian , Hesam Kamyab , Ali Toghroli , Majid Khorami","doi":"10.1016/j.pce.2025.103933","DOIUrl":"10.1016/j.pce.2025.103933","url":null,"abstract":"<div><div>The large coal production and consumption has caused environmental problems worldwide as a source of energy production with irreparable effects on soil, water, and the ecosystem. In addition, producing coal waste in coal washing plants and burying it intensifies the issue in nature. Due to the rising generation of coal waste from various sources, this study utilized several forms of coal waste obtained from a coal-washing plant in the production of both structural concrete (with a water-cement ratio of 0.54) and non-structural concrete (with a water-cement ratio of 0.7). The impact of coal waste on compressive strength (CS) was examined at curing ages of 7, 28, and 56 days. Various percentages of coal waste were substituted for both cement and sand. A superplasticizer was incorporated into the concrete mixtures to enhance the workability and achieve the desired slump and strength levels. According to the compressive strength findings, the ideal replacement level of sand with jig coal waste was 30 %. For 56-day-old specimens, the optimal substitution rates for cement with jig coal waste powder, flotation coal waste, and coal waste ash were found to be 10 %, 10 %, and 20 %, respectively. Notably, adding 10 % coal waste powder and coal waste ash increased compressive strength by 22 %, 23 %, and 44 % at 56 days.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103933"},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conventional and advanced AI-based models in soil moisture prediction

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-11 DOI: 10.1016/j.pce.2025.103944

Marwan Kheimi , Mohammad Zounemat-Kermani

This study evaluates the predictive accuracy of various computational models for Soil Moisture (SM) content, including (i) hard computing models such as mathematical (MLR) and stochastic techniques (AR, ARMA, ARIMA), and (ii) soft computing AI-based models such as shallow learning (NAR, NARX, MLPNN), advanced deep learning (LSTM, DRNN, CNN), and ensemble learning (Bagging, Boosting, and Adaboost). Using data from a sandy clay loam soil area, the models are developed and then compared for accuracy performance, tendency, and computational expense. Results indicate that hard computing models, particularly the stochastic AR model, do not act properly in predicting daily SM values, so that they could not improve the general predictive accuracy in comparison to the Naïve model based on several evaluation metrics. Shallow machine learning models like NAR and MLPNN perform better than the hard computing models, especially when they get the advantage of exogenous input vector (here, precipitation data). Deep learning models (Pearson Correlation Coefficient: PCC >0.9 and RMSE <1.39), especially the LSTM, exhibited higher accuracy than shallow learning models, however, they were the least favorite category in terms of computational cost. On the other hand, ensemble models show the best performance (PCC >0.92, RMSE >1.28) by combining multiple learners' strengths. In summary, the use of ensemble modeling improved the modeling accuracy of RMSE and PCC up to 6 % and 23 % in comparison to stochastic models, respectively.

{"title":"Conventional and advanced AI-based models in soil moisture prediction","authors":"Marwan Kheimi , Mohammad Zounemat-Kermani","doi":"10.1016/j.pce.2025.103944","DOIUrl":"10.1016/j.pce.2025.103944","url":null,"abstract":"<div><div>This study evaluates the predictive accuracy of various computational models for Soil Moisture (SM) content, including (i) hard computing models such as mathematical (MLR) and stochastic techniques (AR, ARMA, ARIMA), and (ii) soft computing AI-based models such as shallow learning (NAR, NARX, MLPNN), advanced deep learning (LSTM, DRNN, CNN), and ensemble learning (Bagging, Boosting, and Adaboost). Using data from a sandy clay loam soil area, the models are developed and then compared for accuracy performance, tendency, and computational expense. Results indicate that hard computing models, particularly the stochastic AR model, do not act properly in predicting daily SM values, so that they could not improve the general predictive accuracy in comparison to the Naïve model based on several evaluation metrics. Shallow machine learning models like NAR and MLPNN perform better than the hard computing models, especially when they get the advantage of exogenous input vector (here, precipitation data). Deep learning models (Pearson Correlation Coefficient: PCC >0.9 and RMSE <1.39), especially the LSTM, exhibited higher accuracy than shallow learning models, however, they were the least favorite category in terms of computational cost. On the other hand, ensemble models show the best performance (PCC >0.92, RMSE >1.28) by combining multiple learners' strengths. In summary, the use of ensemble modeling improved the modeling accuracy of RMSE and PCC up to 6 % and 23 % in comparison to stochastic models, respectively.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103944"},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unravelling the impact of landslide inventory on landslide susceptibility in the Indian Himalaya

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-11 DOI: 10.1016/j.pce.2025.103930

Imran Khan , Ashutosh Kainthola , Harish Bahuguna , Rayees Ahmed , Mohamed Abioui

Landslide susceptibility zonation (LSZ) mapping is heavily influenced by the raster resolution and landslide inventory types. The effect of landslide inventories (polygon and point) at three raster resolutions (12.5 m, 30 m, and 90 m) on LSZ analysis is investigated in this work. The Ramban District sub-basin in Jammu and Kashmir, identified as the most vulnerable area, encompasses 302 landslides. To ensure a robust susceptibility assessment, Yule's coefficient (Yc) was utilized to examine twelve landslide conditioning factors (LCFs) for LSZ preparation. LULC (ESRI & Google) and road variables have the greatest influence at all resolutions, but lithology plays a critical role in lower-resolution polygon-based data. Aspect, geomorphology, slope, and landform exhibit moderate to low effects, which vary with resolution. LULC, roads, and lithology emerge as key influences, whereas drainage, faults, and landforms serve as secondary influences. RR and TWI demonstrate negligible influence on LSZ across all sampling and resolution. LSZ exhibits considerable variation with resolution in point-based inventory. At higher resolutions (12.5 m and 30 m), raster area coverage is below 50 % of vector coverage. Conversely, at 90 m, raster coverage roughly doubles that of vector data, potentially inflating LSZ results. AUC values are higher for point than for polygon sampling. However, for precise mapping, polygon sampling gives a more accurate picture of factors and landslide distribution. This study emphasizes the significance of using polygon sampling to delineate landslide susceptibility in the Himalayas.

{"title":"Unravelling the impact of landslide inventory on landslide susceptibility in the Indian Himalaya","authors":"Imran Khan , Ashutosh Kainthola , Harish Bahuguna , Rayees Ahmed , Mohamed Abioui","doi":"10.1016/j.pce.2025.103930","DOIUrl":"10.1016/j.pce.2025.103930","url":null,"abstract":"<div><div>Landslide susceptibility zonation (LSZ) mapping is heavily influenced by the raster resolution and landslide inventory types. The effect of landslide inventories (polygon and point) at three raster resolutions (12.5 m, 30 m, and 90 m) on LSZ analysis is investigated in this work. The Ramban District sub-basin in Jammu and Kashmir, identified as the most vulnerable area, encompasses 302 landslides. To ensure a robust susceptibility assessment, Yule's coefficient (Yc) was utilized to examine twelve landslide conditioning factors (LCFs) for LSZ preparation. LULC (ESRI & Google) and road variables have the greatest influence at all resolutions, but lithology plays a critical role in lower-resolution polygon-based data. Aspect, geomorphology, slope, and landform exhibit moderate to low effects, which vary with resolution. LULC, roads, and lithology emerge as key influences, whereas drainage, faults, and landforms serve as secondary influences. RR and TWI demonstrate negligible influence on LSZ across all sampling and resolution. LSZ exhibits considerable variation with resolution in point-based inventory. At higher resolutions (12.5 m and 30 m), raster area coverage is below 50 % of vector coverage. Conversely, at 90 m, raster coverage roughly doubles that of vector data, potentially inflating LSZ results. AUC values are higher for point than for polygon sampling. However, for precise mapping, polygon sampling gives a more accurate picture of factors and landslide distribution. This study emphasizes the significance of using polygon sampling to delineate landslide susceptibility in the Himalayas.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103930"},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of long-term hydrological droughts in Turkey's Eastern Black Sea Basin

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-11 DOI: 10.1016/j.pce.2025.103946

Tolga Barış Terzi, Osman Üçüncü

Drought is a complex and multifaceted natural hazard that poses significant risks to ecosystems, economies, and societies. With the intensification of climate change, the frequency and severity of drought events are expected to rise, amplifying their detrimental effects. This study addresses hydrological drought in the Eastern Black Sea Basin (EBSB) in Turkey, a region characterized by high precipitation but largely understudied in terms of drought vulnerability. The Standardized Streamflow Index (SSFI) is employed to assess the drought characteristics across six different hydrological stations in the basin between 1965 and 2011. The sensitivity of the SSFI to the selected probability distribution functions (PDF) was assessed, with Generalized Logistic distribution identified as the most suitable model for the EBSB. The results reveal not only basin-wide drought events but also significant spatial variability in drought severity, particularly during the droughts of 1969–1971, 1994, and 2001. This highlights the region's susceptibility to severe droughts, despite its overall wet climate. The findings underscore the necessity of implementing integrated drought monitoring systems and developing proactive water resource management strategies to mitigate future risks. This study offers new insights into hydrological drought in the EBSB, providing a foundation for future research on drought monitoring and adaptation strategies in similar climates.

{"title":"Evaluation of long-term hydrological droughts in Turkey's Eastern Black Sea Basin","authors":"Tolga Barış Terzi, Osman Üçüncü","doi":"10.1016/j.pce.2025.103946","DOIUrl":"10.1016/j.pce.2025.103946","url":null,"abstract":"<div><div>Drought is a complex and multifaceted natural hazard that poses significant risks to ecosystems, economies, and societies. With the intensification of climate change, the frequency and severity of drought events are expected to rise, amplifying their detrimental effects. This study addresses hydrological drought in the Eastern Black Sea Basin (EBSB) in Turkey, a region characterized by high precipitation but largely understudied in terms of drought vulnerability. The Standardized Streamflow Index (SSFI) is employed to assess the drought characteristics across six different hydrological stations in the basin between 1965 and 2011. The sensitivity of the SSFI to the selected probability distribution functions (PDF) was assessed, with Generalized Logistic distribution identified as the most suitable model for the EBSB. The results reveal not only basin-wide drought events but also significant spatial variability in drought severity, particularly during the droughts of 1969–1971, 1994, and 2001. This highlights the region's susceptibility to severe droughts, despite its overall wet climate. The findings underscore the necessity of implementing integrated drought monitoring systems and developing proactive water resource management strategies to mitigate future risks. This study offers new insights into hydrological drought in the EBSB, providing a foundation for future research on drought monitoring and adaptation strategies in similar climates.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103946"},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating chromite ore deposits: An integrated magnetic and geochemical study in the Kohistan Island Arc of northern Pakistan

IF 3 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth

Pub Date : 2025-04-11 DOI: 10.1016/j.pce.2025.103943

Syed Tallataf Hussain Shah, Arsalan Iftikhar, Syed Mohib Ali, Umair Inayat, Asadullah Khan, Faizan Ur Rehman Qaiser, Javed Iqbal Tanoli

Extensive chromite deposits have been documented and extracted from the ophiolitic belts in northern and western Pakistan. This research investigates chromite ore's magnetic response and associated mineral's concentrations. Measurements from 225 observation stations revealed significant magnetic anomalies, with peak values of 4155.34 nT (Profile A), 3607 nT (Profile B), 3450.3 nT (Profile C), 2247.02 nT (Profile D), and 5146.35 nT (Profile E). Geochemical analysis of fifty-three rock samples showed high concentrations of chromium (324 mg/kg), iron (30,660 mg/kg), nickel (114,000 mg/kg), cobalt (263.8 mg/kg), copper (147.34 mg/kg), and lead (376.4 mg/kg). The highest concentrations of iron, chromium, nickel, cobalt, copper, and lead were found in specific samples within different profiles. Principal Component Analysis suggests that iron, nickel, manganese, and arsenic share a common origin, indicating a standard ore body that may include hematite, magnetite, realgar, or pentlandite. These findings highlight a complex, multi-mineralized ore body with significant economic potential. Pakistan's chromite and associated deposits, especially in nickel and cobalt, are crucial for renewable energy technologies and present large-scale mining opportunities. This study emphasizes the need to systematically explore Pakistan's mineral resources to enhance its role in the global mineral market.

{"title":"Evaluating chromite ore deposits: An integrated magnetic and geochemical study in the Kohistan Island Arc of northern Pakistan","authors":"Syed Tallataf Hussain Shah, Arsalan Iftikhar, Syed Mohib Ali, Umair Inayat, Asadullah Khan, Faizan Ur Rehman Qaiser, Javed Iqbal Tanoli","doi":"10.1016/j.pce.2025.103943","DOIUrl":"10.1016/j.pce.2025.103943","url":null,"abstract":"<div><div>Extensive chromite deposits have been documented and extracted from the ophiolitic belts in northern and western Pakistan. This research investigates chromite ore's magnetic response and associated mineral's concentrations. Measurements from 225 observation stations revealed significant magnetic anomalies, with peak values of 4155.34 nT (Profile A), 3607 nT (Profile B), 3450.3 nT (Profile C), 2247.02 nT (Profile D), and 5146.35 nT (Profile E). Geochemical analysis of fifty-three rock samples showed high concentrations of chromium (324 mg/kg), iron (30,660 mg/kg), nickel (114,000 mg/kg), cobalt (263.8 mg/kg), copper (147.34 mg/kg), and lead (376.4 mg/kg). The highest concentrations of iron, chromium, nickel, cobalt, copper, and lead were found in specific samples within different profiles. Principal Component Analysis suggests that iron, nickel, manganese, and arsenic share a common origin, indicating a standard ore body that may include hematite, magnetite, realgar, or pentlandite. These findings highlight a complex, multi-mineralized ore body with significant economic potential. Pakistan's chromite and associated deposits, especially in nickel and cobalt, are crucial for renewable energy technologies and present large-scale mining opportunities. This study emphasizes the need to systematically explore Pakistan's mineral resources to enhance its role in the global mineral market.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103943"},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0