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One to twelve-month-ahead forecasting of MODIS-derived Qinghai Lake area, using neuro-fuzzy system hybridized by firefly optimization

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Abstract

Lakes, as the main sources of surface water, are of great environmental and ecological importance and largely affect the climatic conditions of the surrounding areas. Lake area fluctuations are very effective on plant and animal biodiversity in the areas covered. Hence, accurate and reliable forecasts of ​lake area might provide the awareness of water and climate resources and the survival of various species dependent on area fluctuations. Using machine learning methods, the current study numerically predicted area fluctuations of ​China’s largest lake, Qinghai, over 1 to 12 months ahead of lead time. To this end, Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images were used to monitor the monthly changes in the area of ​the lake from 2000 to 2021. Predictive inputs included the MODIS-derived lake area time latency specified by the autocorrelation function. The data was divided into two periods of the train (initial 75%) and test (final 25%), and the input combinations were arranged so that the model in the test period could be used to predict 12 scenarios, including forecast horizons for the next 1 to 12 months. The adaptive neuro-fuzzy inference system (ANFIS) was utilized as a predictive model. The firefly algorithm (FA) was also used to optimize ANFIS and improve its accuracy, as a hybrid model ANFIS-FA. Based on evaluation criteria such as root mean square error (RMSE) (477–594 km2) and R2 (88–92%), the results confirmed the acceptable accuracy of the models in all forecast horizons, even long-term horizons (10 months, 11 months, and 12 months). Based on the normalized RMSE criterion (0.095–0.125), the models’ performance was reported to be appropriate. Furthermore, the firefly algorithm improved the prediction accuracy of the ANFIS model by an average of 16.9%. In the inter-month survey, the models had fewer forecast errors in the dry months (February–March) than in the wet months (October–November). Using the current method can provide remarkable information about the future state of lakes, which is very important for managers and planners of water resources, environment, and natural ecosystems. According to the results, the current approach is satisfactory in predicting MODIS-derived fluctuations of Qinghai Lake area and has research value for other lakes.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

Research grant to the corresponding author was received from the Bu-Ali Sina University Deputy of Research and Technology (Grant no. 402243).

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

    Pouya Aghelpour & Vahid Varshavian

  2. Department of Water Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

    Hadigheh Bahrami-Pichaghchi & Reza Norooz-Valashedi

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  1. Pouya Aghelpour
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  2. Hadigheh Bahrami-Pichaghchi
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  3. Vahid Varshavian
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  4. Reza Norooz-Valashedi
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Contributions

Conceptualization: Pouya Aghelpour; methodology: Hadigheh Bahrami-Pichaghchi; software: Pouya Aghelpour and Hadigheh Bahrami-Pichaghchi; formal analysis and investigation: Pouya Aghelpour; preparation and writing of original: Hadigheh Bahrami-Pichaghchi and Pouya Aghelpour; writing including review and editing: Vahid Varshavian and Reza Norooz-Valashedi; resources: Vahid Varshavian and Reza Norooz-Valashedi; supervision: Vahid Varshavian; visualization: Pouya Aghelpour and Hadigheh Bahrami-Pichaghchi.

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Aghelpour, P., Bahrami-Pichaghchi, H., Varshavian, V. et al. One to twelve-month-ahead forecasting of MODIS-derived Qinghai Lake area, using neuro-fuzzy system hybridized by firefly optimization. Environ Sci Pollut Res 31, 22900–22916 (2024). https://doi.org/10.1007/s11356-024-32620-7

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