Skip to main content

Advertisement

SpringerLink
Log in

Trend analysis of temperature data using innovative polygon trend analysis and modeling by gene expression programming

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Presenting temperature data using recently introduced innovative polygon trend analysis (IPTA) can improve our understanding of the effects of climate change. This method was applied to analyze temperature trends at six stations in Turkey: Istanbul (17,064), Ankara (17,131), Bursa (17,116), Iznik (17,661), Gemilik (17,663), and Sakarya (17,069). At station 17,064, there was an increasing trend in temperature data for seven months, while only one month showed a decreasing trend, and the remainder presented no trend. For station 17,131, there was a decreasing trend for two months, an increasing trend for five months, and no trend for the remaining months. At station 17,116, an increasing trend was present for nine months, with a decreasing trend for two months and only one month indicating no trend. An increasing trend over seven months was noted at station 17,661, while two and three months showed a decreasing and no trend, respectively. For station 17,663, there was an increasing trend for nine months, one month showed no trend, and two months presented a decreasing trend. At station 17,069, five, four, and three months showed increasing, decreasing, and no trends, respectively. The gene expression programming (GEP) model was tested to predict the short-term monthly average temperature for this dataset. The proposed GEP model presented good prediction results for all selected stations by tracing the relationship with a coefficient of determination (R-Sq) ≥ 0.90. Trend analysis by IPTA can help understand temperature trends better, aiding future decision-making, and the GEP model can effectively predict short-term values.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

Data that support the findings of this study are available from the Turkish State Meteorological Service, but restrictions apply to the availability of the dataset. Refined data are available from the corresponding author upon request.

References

  • Abhishek, K., Singh, M. P., Ghosh, S., & Anand, A. (2012). Weather forecasting model using artificial neural network. Procedia Technology, 4, 311–318.

    Article  Google Scholar 

  • Achite, M., Ceribasi, G., Ceyhunlu, A. I., Wałęga, A., & Caloiero, T. (2021). The innovative polygon trend analysis (IPTA) as a simple qualitative method to detect changes in environment—Example detecting trends of the total monthly precipitation in semiarid area. Sustainability, 13, 12674.

    Article  Google Scholar 

  • Ahmed, N., Wang, G., Booij, M. J., Ceribasi, G., Bhat, M. S., Ceyhunlu, A. I., & Ahmed, A. (2022). Changes in monthly streamflow in the Hindukush–Karakoram–Himalaya Region of Pakistan using innovative polygon trend analysis. Stochastic Environmental Research and Risk Assessment, 36, 811–830.

    Article  Google Scholar 

  • Alashan, S. (2018). An improved version of innovative trend analyses. Arabian Journal of Geosciences, 11, 1–6.

    Article  Google Scholar 

  • Alfarisy, G. A. F., & Mahmudy, W. F. (2017). Rainfall forecasting in Banyuwangi using adaptive neuro fuzzy inference system. JITeCS Journal of Information Technology and Computer Science, 1, 65–71.

    Article  Google Scholar 

  • Alotaibi, K., Ghumman, A. R., Haider, H., Ghazaw, Y. M., & Shafiquzzaman, M. (2018). Future predictions of rainfall and temperature using GCM and ANN for arid regions: A case study for the Qassim Region, Saudi Arabia. Water, 10, 1260.

    Article  Google Scholar 

  • Arab Amiri, M., & Gocić, M. (2021). Innovative trend analysis of annual precipitation in Serbia during 1946–2019. Environment and Earth Science, 80, 1–15.

    Article  Google Scholar 

  • Azamathulla, H. M., Rathnayake, U., & Shatnawi, A. (2018). Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia. Applied Water Science, 8, 184. https://doi.org/10.1007/s13201-018-0831-6.

    Article  Google Scholar 

  • Ceribasi, G., & Ceyhunlu, A. I. (2021). Analysis of total monthly precipitation of Susurluk Basin in Turkey using innovative polygon trend analysis method. Journal of Water and Climate Change, 12, 1532–1543.

    Article  Google Scholar 

  • Ceribasi, G., & Ceyhunlu, A. I. (2020). Analysis of total monthly precipitation of Susurluk Basin in Turkey using innovative polygon trend analysis method. Journal of Water and Climate Change.

  • Ceribasi, G., Ceyhunlu, A. I., & Ahmed, N. (2021). Analysis of temperature data by using innovative polygon trend analysis and trend polygon star concept methods: A case study for Susurluk Basin, Turkey. Acta Geophysica, 69, 1949–1961. https://doi.org/10.1007/s11600-021-00632-3.

    Article  Google Scholar 

  • Danandeh Mehr, A., Nourani, V., Karimi Khosrowshahi, V., & Ghorbani, M. A. (2019). A hybrid support vector regression-firefly model for monthly rainfall forecasting. International Journal of Environmental Science and Technology, 16.

  • Dong, Z., Jia, W., Sarukkalige, R., Fu, G., Meng, Q., & Wang, Q. (2020). Innovative trend analysis of air temperature and precipitation in the Jinsha River Basin, China. Water, 12, 3293.

    Article  Google Scholar 

  • Fajfar, I., & Tuma, T. (2018). Creation of numerical constants in robust gene expression programming. Entropy, 20, 756.

    Article  Google Scholar 

  • Fenta Mekonnen, D., & Disse, M. (2018). Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques. Hydrology and Earth System Sciences, 22, 2391–2408.

    Article  Google Scholar 

  • Ghumman, A. R., Ghazaw, Y. M., Sohail, A. R., & Watanabe, K. (2011). Runoff forecasting by artificial neural network and conventional model. Alexandria Engineering Journal, 50, 345–350.

    Article  Google Scholar 

  • Güçlü, Y. S., Dabanlı, İ, Şişman, E., & Şen, Z. (2019). Air quality (AQ) identification by innovative trend diagram and AQ index combinations in Istanbul megacity. Atmospheric Pollution Research, 10, 88–96.

    Article  Google Scholar 

  • Hattermann, F. F., Krysanova, V., Gosling, S. N., Dankers, R., Daggupati, P., Donnelly, C., Flörke, M., Huang, S., Motovilov, Y., & Buda, S. (2017). Cross-scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins. Climate Change, 141, 561–576.

    Article  Google Scholar 

  • Hayati, M., & Mohebi, Z. (2007). Application of artificial neural networks for temperature forecasting. International Journal of Electrical and Computer Engineering, 1, 662–666.

    Google Scholar 

  • Kara, E., Özdilek, H. G., & Kara, E. E. (2013). Ambient air quality and asthma cases in Niğde, Turkey. Environmental Science and Pollution Research, 20, 4225–4234.

    Article  CAS  Google Scholar 

  • Karimi, B., Safari, M., Mehr, A. D., & Mohammadi, M. (2019). Monthly rainfall prediction using ARIMA and gene expression programming: A case study in Urmia, Iran. Online Journal of Engineering Science Technologies, 2, 8–17.

    Google Scholar 

  • Kauffman, G. J., & Belden, A. C. (2010). Water quality trends (1970 to 2005) along Delaware streams in the Delaware and Chesapeake Bay watersheds, USA. Water, Air, and Soil Pollution, 208, 345–375.

    Article  CAS  Google Scholar 

  • Kisi, O., & Ay, M. (2014). Comparison of Mann-Kendall and innovative trend method for water quality parameters of the Kizilirmak River, Turkey. Journal of Hydrology, 513, 362–375.

    Article  CAS  Google Scholar 

  • Kovačević, M., Ivanišević, N., Dašić, T., & Marković, L. (2018). Application of artificial neural networks for hydrological modelling in karst. Građevinar, 70, 1–10.

    Google Scholar 

  • Krysanova, V., Vetter, T., Eisner, S., Huang, S., Pechlivanidis, I., Strauch, M., Gelfan, A., Kumar, R., Aich, V., & Arheimer, B. (2017). Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide—a synthesis. Environmental Research Letters, 12, 105002.

    Article  Google Scholar 

  • Landeras, G., López, J. J., Kisi, O., & Shiri, J. (2012). Comparison of gene expression programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain). Energy Conversion and Management, 62, 1–13.

    Article  Google Scholar 

  • Mansouri, I., Güneyisi, E. M., & Mosalam, K. M. (2020). Improved shear strength model for exterior reinforced concrete beam-column joints using gene expression programming. Engineering Structures, 111563. https://doi.org/10.1016/j.engstruct.2020.111563

  • Mehr, A. D., Nourani, V., Kahya, E., Hrnjica, B., Sattar, A. M. A., & Yaseen, Z. M. (2018). Genetic programming in water resources engineering: A state-of-the-art review. Journal of Hydrology, 566, 643–667.

    Article  Google Scholar 

  • Mishra, N., Soni, H. K., Sharma, S., & Upadhyay, A. K. (2018). Development and analysis of artificial neural network models for rainfall prediction by using time-series data. International Journal of Intelligent Systems & Applications, 10.

  • Peng, Y., Zhao, X., Wu, D., Tang, B., Xu, P., Du, X., & Wang, H. (2018). Spatiotemporal variability in extreme precipitation in China from observations and projections. Water, 10, 1089.

    Article  Google Scholar 

  • Pham, T. D., Yoshino, K., & Bui, D. T. (2017). Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multilayer perceptron neural networks. Giscience & Remote Sensing, 54, 329–353.

    Article  Google Scholar 

  • Phuong, D. N. D., Hai, L. M., Dung, H. M., & Loi, N. K. (2022). Temporal trend possibilities of annual rainfall and standardized precipitation index in the Central Highlands, Vietnam. Earth Systems and Environment, 6, 69–85.

    Article  Google Scholar 

  • Rattigan, O. V., Civerolo, K. L., & Felton, H. D. (2017). Trends in wet precipitation, particulate, and gas-phase species in New York State. Atmospheric Pollution Research, 8, 1090–1102.

    Article  Google Scholar 

  • Şan, M., Akçay, F., Linh, N. T. T., Kankal, M., & Pham, Q. B. (2021). Innovative and polygonal trend analyses applications for rainfall data in Vietnam. Theoretical and Applied Climatology, 144, 809–822.

    Article  Google Scholar 

  • Şen, Z. (2021). Conceptual monthly trend polygon methodology and climate change assessments. Hydrological Sciences Journal, 66, 503–512.

    Article  Google Scholar 

  • Şen, Z. (2014). Trend identification simulation and application. Journal of Hydrologic Engineering, 19, 635–642.

    Article  Google Scholar 

  • Şen, Z. (2012). Innovative trend analysis methodology. Journal of Hydrologic Engineering, 17, 1042–1046.

    Article  Google Scholar 

  • Şen, Z., Şişman, E., & Dabanli, I. (2019). Innovative polygon trend analysis (IPTA) and applications. Journal of Hydrology, 575, 202–210.

    Article  Google Scholar 

  • Serinaldi, F., Chebana, F., & Kilsby, C. G. (2020). Dissecting innovative trend analysis. Stochastic Environmental Research and Risk Assessment, 34, 733–754.

    Article  Google Scholar 

  • Shiri, J., Kim, S., & Kisi, O. (2014). Estimation of daily dew point temperature using genetic programming and neural networks approaches. Hydrology Research, 45, 165–181.

    Article  Google Scholar 

  • Shiri, J., Marti, P., & Singh, V. P. (2014). Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning. Hydrological Processes, 28, 1215–1225.

    Article  Google Scholar 

  • Turkish State Meteorological Service. (n.d.). Turkish State Meteorological Service [WWW Document]. https://mgm.gov.tr/eng/forecast-cities.aspx. Accessed 7 July 21.

  • Yao, L., & Lu, N. (2014). Spatiotemporal distribution and short-term trends of particulate matter concentration over China, 2006–2010. Environmental Science and Pollution Research, 21, 9665–9675.

    Article  CAS  Google Scholar 

  • Yaqub, M., Eren, B., & Eyupoglu, V. (2019). Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes. Environmental Engineering Research. https://doi.org/10.4491/eer.2019.085

  • Yaqub, M., & Lee, S. H. (2020a). Experimental and neural network modeling of micellar enhanced ultrafiltration for arsenic removal from aqueous solution. Environmental Engineering Research. https://doi.org/10.4491/eer.2019.261

  • Yaqub, M., & Lee, S. H. (2020b). Micellar enhanced ultrafiltration (MEUF) of mercury-contaminated wastewater: Experimental and artificial neural network modeling. Journal of Water Process Engineering, 33, 101046.

  • Yaqub, M., Woo, C., & Lee, W. (2021). Optimization of hypochlorous acid generation by HCl electrolysis through response surface methodology and artificial neural networks. Journal of Environmental Chemical Engineering, 105826.

  • Yassin, M. A., Alazba, A. A., & Mattar, M. A. (2016). Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate. Agricultural Water Management, 163, 110–124.

    Article  Google Scholar 

  • Yurekli, K. (2021). Scrutinizing variability in full and partial rainfall time series by different approaches. Natural Hazards, 105, 2523–2542.

    Article  Google Scholar 

  • Zhang, H., Zhang, L. L., Li, J., An, R. D., & Deng, Y. (2018). Climate and hydrological change characteristics and applicability of GLDAS data in the Yarlung Zangbo River Basin, China. Water, 10, 254.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Center for Islamic Economics and Finance, Sakarya University, Sakarya, Turkey

    Ali Can Yenice

  2. Department of Environmental Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea

    Muhammad Yaqub

Authors
  1. Ali Can Yenice
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Yaqub
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Yaqub.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Highlights

• Conducted monthly average temperature trend through innovative polygon trend analysis (IPTA).

• Applied and tested the feasibility of the gene expression programming (GEP) model.

• Predicted monthly average temperature for short-term future values using proposed GEP.

• Results showed that IPTA and GEP could trace trends and predict monthly average temperature successfully.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 290 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yenice, A.C., Yaqub, M. Trend analysis of temperature data using innovative polygon trend analysis and modeling by gene expression programming. Environ Monit Assess 194, 543 (2022). https://doi.org/10.1007/s10661-022-10156-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-022-10156-y

Keywords

Search

Navigation