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Márquez-Grajales A, Villegas-Vega R, Salas-Martínez F, Acosta-Mesa HG, Mezura-Montes E. Characterizing drought prediction with deep learning: A literature review. MethodsX 2024; 13:102800. [PMID: 38989261 PMCID: PMC11234152 DOI: 10.1016/j.mex.2024.102800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/11/2024] [Indexed: 07/12/2024] Open
Abstract
Drought prediction is a complex phenomenon that impacts human activities and the environment. For this reason, predicting its behavior is crucial to mitigating such effects. Deep learning techniques are emerging as a powerful tool for this task. The main goal of this work is to review the state-of-the-art for characterizing the deep learning techniques used in the drought prediction task. The results suggest that the most widely used climate indexes were the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Regarding the multispectral index, the Normalized Difference Vegetation Index (NDVI) is the indicator most utilized. On the other hand, countries with a higher production of scientific knowledge in this area are located in Asia and Oceania; meanwhile, America and Africa are the regions with few publications. Concerning deep learning methods, the Long-Short Term Memory network (LSTM) is the algorithm most implemented for this task, either implemented canonically or together with other deep learning techniques (hybrid methods). In conclusion, this review reveals a need for more scientific knowledge about drought prediction using multispectral indices and deep learning techniques in America and Africa; therefore, it is an opportunity to characterize the phenomenon in developing countries.
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Affiliation(s)
- Aldo Márquez-Grajales
- INFOTEC Center for Research and Innovation in Information and Communication Technologies, Circuito Tecnopolo Sur, No 112, Fracc. Tecnopolo Pocitos, Aguascalientes, 20326, Aguascalientes, México
| | - Ramiro Villegas-Vega
- Artificial Intelligence Research Institute, University of Veracruz, Campus Sur Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, 91097, Xalapa, Veracruz, México
| | | | - Héctor-Gabriel Acosta-Mesa
- Artificial Intelligence Research Institute, University of Veracruz, Campus Sur Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, 91097, Xalapa, Veracruz, México
| | - Efrén Mezura-Montes
- Artificial Intelligence Research Institute, University of Veracruz, Campus Sur Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, 91097, Xalapa, Veracruz, México
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Yaseen ZM, Melini Wan Mohtar WH, Homod RZ, Alawi OA, Abba SI, Oudah AY, Togun H, Goliatt L, Ul Hassan Kazmi SS, Tao H. Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm. CHEMOSPHERE 2024; 352:141329. [PMID: 38296204 DOI: 10.1016/j.chemosphere.2024.141329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/09/2024] [Accepted: 01/28/2024] [Indexed: 02/09/2024]
Abstract
This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.
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Affiliation(s)
- Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Wan Hanna Melini Wan Mohtar
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM, Bangi, Selangor, Malaysia; Environmental Management Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
| | - Raad Z Homod
- Department of Oil and Gas Engineering, Basrah University for Oil and Gas, Basra, Iraq.
| | - Omer A Alawi
- Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Malaysia.
| | - Sani I Abba
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
| | - Atheer Y Oudah
- Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Nasiriyah, 64001, Iraq; Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, 64001, Iraq.
| | - Hussein Togun
- Department of Mechanical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq.
| | - Leonardo Goliatt
- Computational and Applied Mechanics Department, Federal University of Juiz de Fora, 36036-900, Brazil.
| | - Syed Shabi Ul Hassan Kazmi
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, and Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University, Shantou, 515063, China.
| | - Hai Tao
- School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Institute of Big Data Application and Artificial Intelligence, Qiannan Normal University for Nationalities, Duyun, 558000, Guizhou, China; Faculty of Data Science and Information Technology, INTI International University, 71800, Malaysia.
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Costache R, Arabameri A, Costache I, Crăciun A, Islam ARMT, Abba SI, Sahana M, Pandey M, Tin TT, Pham BT. Flood hazard potential evaluation using decision tree state-of-the-art models. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2024; 44:439-458. [PMID: 37357220 DOI: 10.1111/risa.14179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute-weights of evidence (forest-PA-WOE), best first decision tree-WOE, alternating decision tree-WOE, and logistic regression-WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest-PA-WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data-scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.
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Affiliation(s)
- Romulus Costache
- Department of Civil Engineering, Transilvania University of Brasov, Brasov, Romania
- Danube Delta National Institute for Research and Development, Tulcea, Romania
- National Institute of Hydrology and Water Management, Bucharest, Romania
- Research Institute of the University of Bucharest, Bucharest, Romania
| | - Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, Tehran, Iran
| | - Iulia Costache
- Faculty of Geography, University of Bucharest, Bucharest, Romania
| | - Anca Crăciun
- Danube Delta National Institute for Research and Development, Tulcea, Romania
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur, Bangladesh
- Department of Development Studies, Daffodil International University, Dhaka, Bangladesh
| | - Sani Isah Abba
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
| | - Mehebub Sahana
- Department of Geography, University of Manchester, Manchester, UK
| | - Manish Pandey
- University Center for Research & Development (UCRD), Chandigarh University, Mohali, Punjab, India
- Department of Civil Engineering, University Institute of Engineering, Chandigarh University, Mohali, Punjab, India
| | - Tran Trung Tin
- Department of Information Technology, Swinburne Vietnam - FPT University, Danang, Vietnam
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Hameed MM, Razali SFM, Mohtar WHMW, Rahman NA, Yaseen ZM. Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America. PLoS One 2023; 18:e0290891. [PMID: 37906556 PMCID: PMC10617742 DOI: 10.1371/journal.pone.0290891] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 08/15/2023] [Indexed: 11/02/2023] Open
Abstract
The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.
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Affiliation(s)
- Mohammed Majeed Hameed
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
| | - Siti Fatin Mohd Razali
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
- Green Engineering and Net Zero Solution (GREENZ), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
| | - Wan Hanna Melini Wan Mohtar
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
- Green Engineering and Net Zero Solution (GREENZ), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
| | - Norinah Abd Rahman
- Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
- Green Engineering and Net Zero Solution (GREENZ), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia
| | - Zaher Mundher Yaseen
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
- Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
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Shakeri R, Amini H, Fakheri F, Ketabchi H. Assessment of drought conditions and prediction by machine learning algorithms using Standardized Precipitation Index and Standardized Water-Level Index (case study: Yazd province, Iran). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:101744-101760. [PMID: 37656297 DOI: 10.1007/s11356-023-29522-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/22/2023] [Indexed: 09/02/2023]
Abstract
Drought as a natural phenomenon has always been a serious threat to regions with hot and dry climates. One of the major effects of drought is the drop in groundwater level. This paper focused on the SPI (Standardized Precipitation Index) and SWI (Standardized Water-Level Index) to assess meteorological and hydrological drought, respectively. In the first part, we used different time frames of SPI (3, 6, 12, and 24 months) to investigate drought in Yazd, a dry province in the center of Iran for 29 years (1990-2018). Then, in the second part, the relationship between SPI and SWI was investigated in the three aquifers of Yazd by some rain gauge stations and the closest observation wells to them. In addition to using SPI and SWI, we also used different machine learning (ML) algorithms to predict drought conditions including linear model and six non-linear models of K_Nearest_Neighbors, Gradient_Boosting, Decision_Tree, XGBoost, Random_Forest, and Neural_Net. To evaluate the accuracy of the mentioned models, three statistical indicators including Score, RMSE, and MAE were used. Based on the results of the first part, Yazd province has changed from mild wet to mild drought in terms of meteorological drought (the amount of rainfall according to SPI), and this condition can worsen due to climate change. The models used in ML showed that SPI-6 (score ave = 0.977), SPI-3 (score ave = 0.936), SPI-24 (score ave = 0.571), and SPI-12 (score ave = 0.413) indices had the highest accuracy, respectively. The models of Neural_Net (score ave = 0.964-RMSE ave = 0.020-MAE ave = 0.077) and Gradient_Boosting (score ave = 0.551-RMSE ave = 0.124-MAE ave = 0.248) had the highest and lowest accuracy in prediction of the SPI in all four-time scales. Based on the results of the second part, about the SWI, Random_Forest model (score = 0.929-RMSE = 0.052-MAE = 0.150) and model of Neural_Net (score = 0.755-RMSE = 0.235-MAE = 0.456) had the highest and lowest accuracy, respectively. Also, hydrological drought (reduction of the groundwater level) of the region has been much more severe, and according to the low correlation coefficient of average SPI and SWI (R2 = 0.14), we found that the uncontrolled pumping wells, as a main factor than a shortage of rainfall, have aggravated the hydrological drought, and this region is at risk of becoming a more arid region in the future.
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Affiliation(s)
- Reza Shakeri
- Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Hossein Amini
- Engineering Department, Cardiff University, Cardiff, UK
| | - Farshid Fakheri
- Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Hamed Ketabchi
- Department of Water Engineering and Management, Tarbiat Modares University, Tehran, Iran.
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Li S, Xie J, Yang X, Jing X. Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 87:2756-2775. [PMID: 37318922 PMCID: wst_2023_162 DOI: 10.2166/wst.2023.162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Reliable drought prediction plays a significant role in drought management. Applying machine learning models in drought prediction is getting popular in recent years, but applying the stand-alone models to capture the feature information is not sufficient enough, even though the general performance is acceptable. Therefore, the scholars tried the signal decomposition algorithm as a data pre-processing tool, and coupled it with the stand-alone model to build 'decomposition-prediction' model to improve the performance. Considering the limitations of using the single decomposition algorithm, an 'integration-prediction' model construction method is proposed in this study, which deeply combines the results of multiple decomposition algorithms. The model tested three meteorological stations in Guanzhong, Shaanxi Province, China, where the short-term meteorological drought is predicted from 1960 to 2019. The meteorological drought index selects the Standardized Precipitation Index on a 12-month time scale (SPI-12). Compared with stand-alone models and 'decomposition-prediction' models, the 'integration-prediction' models present higher prediction accuracy, smaller prediction error and better stability in the results. This new 'integration-prediction' model provides attractive value for drought risk management in arid regions.
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Affiliation(s)
- Shaoxuan Li
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China E-mail:
| | - Jiancang Xie
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China E-mail:
| | - Xue Yang
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China E-mail:
| | - Xin Jing
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China E-mail:
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Zhao L, Zhao X, Li Y, Shi Y, Zhou H, Li X, Wang X, Xing X. Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:22396-22412. [PMID: 36289123 DOI: 10.1007/s11356-022-23786-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources; this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ETO; the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R2 of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.
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Affiliation(s)
- Long Zhao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Xinbo Zhao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Yuanze Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Yi Shi
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Hanmi Zhou
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Xiuzhen Li
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang, 471000, Henan Province, China
| | - Xiaodong Wang
- College of Agriculture, Henan University of Science and Technology, Henan Province, Luoyang, 471000, China
| | - Xuguang Xing
- Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling, 712100, Shaanxi Province, China.
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Wang Y, Qiu R, Tao Y, Wu J. Influence of the impoundment of the Three Gorges Reservoir on hydrothermal conditions for fish habitat in the Yangtze River. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:10995-11011. [PMID: 36087184 DOI: 10.1007/s11356-022-22930-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/04/2022] [Indexed: 06/15/2023]
Abstract
The thermal regimes of rivers play an important role in the overall health of aquatic ecosystems. Modifications to water temperature regimes resulting from dams and reservoirs have important consequences for river ecosystems. This study investigates the impacts of the impoundment of the Three Gorges Reservoir (TGR) on the water temperature regime of fish spawning habitats in the middle reach of the Yangtze River, China. Mike 11 model is used to analyze the temporal and spatial variation of water temperatures of the expanse of 400 km along the river, from Yichang to Chenglingji. The water temperature alterations caused by the operation of the TGR are assessed with river temperature metrics. The impact on spawning habitats due to water temperature variation was also discussed in different impoundments of the TGR. The results show that the TGR has significantly altered the downstream water temperature regime, affecting the baseline deviation and phase shift of the water temperature. Such impacts on the thermal regime of the river varied with the impoundment level. The effects of the TGR on the water temperature regime decreased as the distance from the structure to the sample site increased. The water temperature regime alterations have led to the delay of the spawning times of the four famous major carp (FFMC) species. The results could be used to identify the magnitudes of water temperature alterations induced by reservoirs in the Yangtze River and provide useful information to design ecological operations for the protection of river ecosystem integrity in regulated rivers.
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Affiliation(s)
- Yuankun Wang
- School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing, People's Republic of China.
| | - Rujian Qiu
- School of Earth Sciences and Engineering, Nanjing University, Nanjing, People's Republic of China
| | - Yuwei Tao
- School of Earth Sciences and Engineering, Nanjing University, Nanjing, People's Republic of China
| | - Jichun Wu
- School of Earth Sciences and Engineering, Nanjing University, Nanjing, People's Republic of China
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Madaki Z, Abacioglu N, Usman AG, Taner N, Sehirli AO, Abba SI. Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010079. [PMID: 36676028 PMCID: PMC9866913 DOI: 10.3390/life13010079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022]
Abstract
The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.
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Affiliation(s)
- Zachariah Madaki
- Department of Pharmacology, Faculty of Pharmacy, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
| | - Nurettin Abacioglu
- Department of Pharmacology, Faculty of Pharmacy, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
| | - A. G. Usman
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
- Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
- Correspondence: (A.G.U.); (S.I.A.)
| | - Neda Taner
- Department of Clinical Pharmacy, Faculty of Pharmacy, Istanbul Medipol University, 34810 Istanbul, Türkiye
| | - Ahmet. O. Sehirli
- Department of Pharmacology, Faculty of Dentistry, Nicosia, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
| | - S. I. Abba
- Interdisciplinary Research Centre for Membrane and Water Security, Faculty of Petroleum and Minerals, King Fahd University, Dhahran 31261, Saudi Arabia
- Correspondence: (A.G.U.); (S.I.A.)
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Vishwakarma DK, Ali R, Bhat SA, Elbeltagi A, Kushwaha NL, Kumar R, Rajput J, Heddam S, Kuriqi A. Pre- and post-dam river water temperature alteration prediction using advanced machine learning models. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:83321-83346. [PMID: 35763134 PMCID: PMC9244425 DOI: 10.1007/s11356-022-21596-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/16/2022] [Indexed: 04/12/2023]
Abstract
Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water's temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. In this study, we predict the daily water temperature of the Yangtze River at Cuntan. Thus, this work reveals the potential of machine learning models, namely, M5 Pruned (M5P), Random Forest (RF), Random Subspace (RSS), and Reduced Error Pruning Tree (REPTree). The best and effective input variables combinations were determined based on the correlation coefficient. The outputs of the various machine learning algorithm models were compared with recorded daily water temperature data using goodness-of-fit criteria and graphical analysis to arrive at a final comparison. Based on a number of criteria, numerical comparison between the models revealed that M5P model performed superior (R2 = 0.9920, 0.9708; PCC = 0.9960, 0.9853; MAE = 0.2387, 0.4285; RMSE = 0.3449, 0.4285; RAE = 6.2573, 11.5439; RRSE = 8.0288, 13.8282) in pre-impact and post-impact spam, respectively. These findings suggest that a huge wave of dam construction in the previous century altered the hydrologic regimes of large and minor rivers. This study will be helpful for the ecologists and river experts in planning new reservoirs to maintain the flows and minimize the water temperature concerning spillway operation. Finally, our findings revealed that these algorithms could reliably estimate water temperature using a day lag time input in water level. They are cost-effective techniques for forecasting purposes.
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Affiliation(s)
- Dinesh Kumar Vishwakarma
- Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, 263145 India
| | - Rawshan Ali
- Department of Petroleum, Koya Technical Institute, Erbil Polytechnic University, Erbil, 44001 Iraq
| | - Shakeel Ahmad Bhat
- College of Agricultural Engineering and Technology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu and Kashmir 190025 India
| | - Ahmed Elbeltagi
- Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516 Egypt
| | - Nand Lal Kushwaha
- Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Rohitashw Kumar
- College of Agricultural Engineering and Technology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu and Kashmir 190025 India
| | - Jitendra Rajput
- Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012 India
| | - Salim Heddam
- Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria
| | - Alban Kuriqi
- CERIS, Instituto Superior Técnico, University of Lisbon, 1649-004 Lisbon, Portugal
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11
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Malik A, Tikhamarine Y, Sihag P, Shahid S, Jamei M, Karbasi M. Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:71270-71289. [PMID: 35597830 DOI: 10.1007/s11356-022-20837-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST5), 15-cm (ST15), and 30-cm (ST30) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e. support vector machine (SVM), multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) optimized with slime mould algorithm (SMA), particle swarm optimization (PSO), and spotted hyena optimizer (SHO) algorithms. Five scenarios with different input variables were constructed using daily meteorological parameters, and the optimal one was extracted by exploiting the GT (gamma test). The feasibility of the proposed hybrid SVM, MLP, and ANFIS models was inspected based on performance metrics and visual interpretation. According to the results, the SVM-SMA model yields better estimates than other models at 5-cm, 15-cm, and 30-cm soil depths, respectively, for scenario 5 in the validation phase. Furthermore, conferring to the results, the SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering (Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index of agreement) and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site.
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Affiliation(s)
- Anurag Malik
- Punjab Agricultural University, Regional Research Station, Bathinda-151001, Punjab, India.
| | - Yazid Tikhamarine
- Department of Science and Technology, University of Tamanrasset, Sersouf, Tamanrasset, Algeria
- Southern Public Works Laboratory (LTPS), Tamanrasset Unity, Tamanrasset, Algeria
| | - Parveen Sihag
- Department of Civil Engineering, Chandigarh University, Mohali, 141003, Punjab, India
| | - Shamsuddin Shahid
- School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia
| | - Mehdi Jamei
- Faculty of Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan, Iran
| | - Masoud Karbasi
- Water Engineering Department, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
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12
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Wen L, Song Q. The forecasting model research of rural energy transformation in Henan Province based on STIRPAT model. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:75550-75565. [PMID: 35657551 DOI: 10.1007/s11356-022-21119-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
In order to find the model of rural energy transformation in Henan Province. In this paper, Tapio decoupling model is employed to investigate the pivotal factors affecting rural power consumption (PC) and total energy consumption (TEC) in Henan Province. In addition, PSO-BP is used to predict the values of each influencing factor in 2020-2025. Last, the STIRPAT model is used to forecast TEC and PC from 2020 to 2025 based on the data of rural energy consumption in Henan Province from 2009-2019. The results show that other factors besides population promote TEC and PC to different degrees. Moreover, the influencing factors, TEC and PC, form a virtuous cycle of mutual promotion. Then, TEC and PC consumption show an increasing trend year by year in 2020-2025. It is worth noting that after 2022, the variation of PC is greater than that of TEC. To sum up, improving rural electrification level is a necessary way to realize its low-carbon energy transition.
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Affiliation(s)
- Lei Wen
- Department of Economics and Management, North China Electric Power University, Baoding, 071000, China
| | - Qianqian Song
- Department of Economics and Management, North China Electric Power University, Baoding, 071000, China.
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13
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Gupta D, Natarajan N, Berlin M. Short-term wind speed prediction using hybrid machine learning techniques. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:50909-50927. [PMID: 34251573 DOI: 10.1007/s11356-021-15221-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/27/2021] [Indexed: 06/13/2023]
Abstract
Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have been deployed for short-term wind speed prediction. The twin support vector regression (TSVR), primal least squares twin support vector regression (PLSTSVR), iterative Lagrangian twin parametric insensitive support vector regression (ILTPISVR), extreme learning machine (ELM), random vector functional link (RVFL), and large-margin distribution machine-based regression (LDMR) models have been adopted in predicting the short-term wind speed collected from five stations named as Chennai, Coimbatore, Madurai, Salem, and Tirunelveli in Tamil Nadu, India. Further to check the applicability of the models, the performance of the models was compared based on various performance measures like RMSE, MAPE, SMAPE, MASE, SSE/SST, SSR/SST, and R2. The results suggest that LDMR outperforms other models in terms of its prediction accuracy and ELM is computationally faster compared to other models.
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Affiliation(s)
- Deepak Gupta
- Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Papum Pare, Arunachal Pradesh, 791112, India
| | - Narayanan Natarajan
- Department of Civil Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, 642003, India.
| | - Mohanadhas Berlin
- Department of Civil Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Papum Pare, Arunachal Pradesh, 791112, India
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14
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Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing. HYDROLOGY 2022. [DOI: 10.3390/hydrology9070115] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models.
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15
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Abstract
Owing to climate change, industrial pollution, and population gathering, the air quality status in many places in China is not optimal. The continuous deterioration of air-quality conditions has considerably affected the economic development and health of China’s people. However, the diversity and complexity of the factors which affect air pollution render air quality monitoring data complex and nonlinear. To improve the accuracy of prediction of the air quality index (AQI) and obtain more accurate AQI data with respect to their nonlinear and nonsmooth characteristics, this study introduces an air quality prediction model based on the empirical mode decomposition (EMD) of LSTM and uses improved particle swarm optimization (IPSO) to identify the optimal LSTM parameters. First, the model performed the EMD decomposition of air quality data and obtained uncoupled intrinsic mode function (IMF) components after removing noisy data. Second, we built an EMD–IPSO–LSTM air quality prediction model for each IMF component and extracted prediction values. Third, the results of validation analyses of the algorithm showed that compared with LSTM and EMD–LSTM, the improved model had higher prediction accuracy and improved the model fitting effect, which provided theoretical and technical support for the prediction and management of air pollution.
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16
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Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects. ENERGIES 2021. [DOI: 10.3390/en14165206] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.
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17
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Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach. Processes (Basel) 2021. [DOI: 10.3390/pr9071166] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.
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