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Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction

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  • Martin, G., Martin-Clouaire, R. & Duru, M. Farming system design to feed the changing world. A review. Agron. Sustain. Dev. 33, 131–149 (2013).

    Google Scholar 

  • McElwee, G. & Bosworth, G. Exploring the strategic skills of farmers across a typology of farm diversification approaches. J. Farm Manag. 13, 819–838 (2010).

    Google Scholar 

  • Maghrebi, M. et al. Iran’s agriculture in the anthropocene. Earth’s Future. https://doi.org/10.1029/2020EF001547 (2020).

    Article 

    Google Scholar 

  • Raorane, A. A. & Kulkarni, R. V. Data mining: An effective tool for yield estimation in the agricultural sector. Int. J. Emerg. Trends Technol. Comput. Sci. 1, 1–4 (2012).

    Google Scholar 

  • Gonzalez-Sanchez, A., Frausto-Solis, J. & Ojeda-Bustamante, W. Attribute selection impact on linear and nonlinear regression models for crop yield prediction. Sci. World J. 2014, 509429 (2014).

    Google Scholar 

  • Salman, S. A. et al. Changes in climatic water availability and crop water demand for Iraq region. Sustainability 12, 3437 (2020).

    Google Scholar 

  • Mahmood, N., Arshad, M., Kächele, H., Ullah, A. & Müller, K. Economic efficiency of rainfed wheat farmers under changing climate: Evidence from Pakistan. Environ. Sci. Pollut. Res. 27, 34453–34467 (2020).

    Google Scholar 

  • Pracha, A. S. & Volk, T. A. An edible energy return on investment (EEROI) analysis of wheat and rice in Pakistan. Sustainability 3, 2358–2391 (2011).

    Google Scholar 

  • Canadell, J. et al. Abberton, M., Conant, R., & Batello, C. (Eds.). (2010). Grassland carbon sequestration: Management, policy and economics. Food and Agriculture Organization of the United Nations, Integrated Crop Management, Vol. 11–2010. Ahlstrom, A., Raupach, M., Schurgers. Sensit. A Semi-Arid Grassl. To Extrem. Precip. Events 127, 6 (2021).

    Google Scholar 

  • Canton, H. Food and Agriculture Organization of the United Nations—FAO. In The Europa Directory of International Organizations 2021 (ed. Canton, H.) 297–305 (Routledge, 2021).

    Google Scholar 

  • Abdullah, A. et al. Potential for sustainable utilisation of agricultural residues for bioenergy production in Pakistan: An overview. J. Clean. Prod. 287, 125047 (2020).

    Google Scholar 

  • Mughal, I. et al. Protein quantification and enzyme activity estimation of Pakistani wheat landraces. PLoS ONE 15, e0239375 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dorosh, P. & Salam, A. Wheat markets and price stabilisation in Pakistan: An analysis of policy options. Pak. Dev. Rev. 47, 71–87 (2008).

    Google Scholar 

  • Fowke, V. The National Policy and the Wheat Economy (University of Toronto Press, 2019).

    Google Scholar 

  • Hussain, S. et al. Study the effects of COVID-19 in Punjab, Pakistan using space-time scan statistic for policy measures in regional agriculture and food supply chain. Environ. Sci. Pollut. Res. Int. 20, 1–14 (2021).

    Google Scholar 

  • Sajjad, S. A. Story of Pakistan’s Elite Wheat (The Express Tribune, 2017).

    Google Scholar 

  • Durgun, Y. Ö., Gobin, A., Duveiller, G. & Tychon, B. A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time. Int. J. Appl. Earth Obs. Geoinf. 86, 101988 (2020).

    Google Scholar 

  • Vannoppen, A. et al. Wheat yield estimation from NDVI and regional climate models in Latvia. Remote Sens. 12, 2206 (2020).

    ADS 

    Google Scholar 

  • Irmak, A. et al. Artificial neural network model as a data analysis tool in precision farming. Trans. ASABE 49, 2027–2037 (2006).

    Google Scholar 

  • Bannerjee, G., Sarkar, U., Das, S. & Ghosh, I. Artificial intelligence in agriculture: A literature survey. Int. J. Sci. Res. Comput. Sci. Appl. Manag. Stud. 7, 1–6 (2018).

    Google Scholar 

  • Patrício, D. I. & Rieder, R. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Comput. Electron. Agric. 153, 69–81 (2018).

    Google Scholar 

  • Yaseen, Z. M. et al. Prediction of evaporation in arid and semi-arid regions: A comparative study using different machine learning models. Eng. Appl. Comput. Fluid Mech. 14, 70–89 (2019).

    Google Scholar 

  • Bauer, M. E. The role of remote sensing in determining the distribution and yield of crops. In Advances in Agronomy (ed. Sparks, D. L.) 271–304 (Elsevier, 1975). https://doi.org/10.1016/s0065-2113(08)70012-9.

    Chapter 

    Google Scholar 

  • Dempewolf, J. et al. Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. Remote Sens. 6, 9653–9675 (2014).

    ADS 

    Google Scholar 

  • Hamid, N., Pinckney, T. C., Gnaegy, S. & Valdes, A. The Wheat Economy of Pakistan: Setting and Prospects (IFPRI, 2015).

    Google Scholar 

  • Muhammad, K. Description of the Historical Background of Wheat Improvement in Baluchistan, Pakistan (Agriculture Research Institute (Sariab, Quetta, Baluchistan, Pakistan), 1989).

    Google Scholar 

  • Iqbal, N., Bakhsh, K., Maqbool, A. & Abid Shohab, A. Use of the ARIMA model for forecasting wheat area and production in Pakistan. J. Agric. Soc. Sci. 1, 120–122 (2005).

    Google Scholar 

  • Sher, F. & Ahmad, E. Forecasting wheat production in Pakistan. LAHORE J. Econ. 13, 57–85 (2008).

    Google Scholar 

  • Khan, N. et al. Determination of cotton and wheat yield using the standard precipitation evaporation index in Pakistan. Arab. J. Geosci. 14, 1–16 (2021).

    Google Scholar 

  • Rahman, M. M., Haq, N. & Rahman, R. M. Machine learning facilitated rice prediction in Bangladesh. In 2014 Annual Global Online Conference on Information and Computer Technology. https://doi.org/10.1109/gocict.2014.9 (2014).

  • Chen, C. & Mcnairn, H. A neural network integrated approach for rice crop monitoring. Int. J. Remote Sens. 27, 1367–1393 (2006).

    Google Scholar 

  • Kaul, M., Hill, R. L. & Walthall, C. Artificial neural networks for corn and soybean yield prediction. Agric. Syst. 85, 1–18 (2005).

    Google Scholar 

  • Deo, R. C., Samui, P., Kisi, O. & Yaseen, Z. M. Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation: Theory and Practice of Hazard Mitigation (Springer Nature, 2020).

    Google Scholar 

  • Sanikhani, H. et al. Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors. Comput. Electron. Agric. 152, 242–260 (2018).

    Google Scholar 

  • Hai, T. et al. Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model. IEEE Access 8, 12026–12042 (2020).

    Google Scholar 

  • Ramos, A. P. M. et al. A random forest ranking approach to predict yield in maize with UAV-based vegetation spectral indices. Comput. Electron. Agric. 178, 105791 (2020).

    Google Scholar 

  • Suchithra, M. S. & Pai, M. L. Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Inf. Process. Agric. 7, 72–82 (2020).

    Google Scholar 

  • Feng, Z., Huang, G. & Chi, D. Classification of the complex agricultural planting structure with a semi-supervised extreme learning machine framework. Remote Sens. 12, 3708 (2020).

    ADS 

    Google Scholar 

  • Tur, R. & Yontem, S. A comparison of soft computing methods for the prediction of wave height parameters. Knowl. Based Eng. Sci. 2, 31–46 (2021).

    Google Scholar 

  • Yaseen, Z. M., Ali, M., Sharafati, A., Al-Ansari, N. & Shahid, S. Forecasting standardized precipitation index using data intelligence models: Regional investigation of Bangladesh. Sci. Rep. 11, 1–25 (2021).

    Google Scholar 

  • Sharafati, A., Asadollah, S. B. H. S. & Neshat, A. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125468 (2020).

    Article 

    Google Scholar 

  • Huang, G.-B., Zhu, Q.-Y. & Siew, C.-K. Extreme learning machine: Theory and applications. Neurocomputing 70, 489–501 (2006).

    Google Scholar 

  • Adnan, R. M. et al. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowl. Based Syst. 230, 107379 (2021).

    Google Scholar 

  • Yaseen, Z. M. et al. Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. J. Hydrol. 542, 603–614 (2016).

    ADS 

    Google Scholar 

  • Prasad, R., Deo, R. C., Li, Y. & Maraseni, T. Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors. Soil Tillage Res. https://doi.org/10.1016/j.still.2018.03.021 (2018).

    Article 

    Google Scholar 

  • Tiyasha, T. et al. Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models. Mar. Pollut. Bull. 170, 112639 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • Ali, M. et al. Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology. Energy Rep. 7, 6700–6717 (2021).

    Google Scholar 

  • Khozani, Z. S. et al. Determination of compound channel apparent shear stress: Application of novel data mining models. J. Hydroinform. 21, 798–811 (2019).

    MathSciNet 

    Google Scholar 

  • Dorigo, M. & Di Caro, G. Ant colony optimization: A new meta-heuristic. In Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999. https://doi.org/10.1109/CEC.1999.782657 (1999).

  • Mullen, R. J., Monekosso, D., Barman, S. & Remagnino, P. A review of ant algorithms. Expert Syst. Appl. https://doi.org/10.1016/j.eswa.2009.01.020 (2009).

    Article 

    Google Scholar 

  • Sweetlin, J. D., Nehemiah, H. K. & Kannan, A. Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images. Comput. Methods Prog. Biomed. https://doi.org/10.1016/j.cmpb.2017.04.009 (2017).

    Article 

    Google Scholar 

  • Cordon, O., Herrera, F. & Stützle, T. A review on the ant colony optimization metaheuristic: Basis, models and new trends. Mathw. Comput. 9, 2–3 (2002).

    MathSciNet 
    MATH 

    Google Scholar 

  • Singh, G., Kumar, N. & Kumar Verma, A. Ant colony algorithms in MANETs: A review. J. Netw. Comput. Appl. https://doi.org/10.1016/j.jnca.2012.07.018 (2012).

    Article 

    Google Scholar 

  • Kumar, S., Solanki, V. K., Choudhary, S. K., Selamat, A. & González Crespo, R. Comparative study on ant colony optimization (ACO) and K-means clustering approaches for jobs scheduling and energy optimization model in internet of things (IoT). Int. J. Interact. Multimed. Artif. Intell. 6, 107 (2020).

    Google Scholar 

  • Paniri, M., Dowlatshahi, M. B. & Nezamabadi-pour, H. MLACO: A multi-label feature selection algorithm based on ant colony optimization. Knowl. Based Syst. 192, 105285 (2020).

    Google Scholar 

  • Yaseen, Z. M., Sulaiman, S. O., Deo, R. C. & Chau, K.-W. An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. J. Hydrol. 569, 387–408 (2019).

    ADS 

    Google Scholar 

  • Manju Parkavi, R., Shanthi, M. & Bhuvaneshwari, M. C. Recent trends in ELM and MLELM: A review. Adv. Sci. Technol. Eng. Syst. https://doi.org/10.25046/aj020108 (2017).

    Article 

    Google Scholar 

  • Araba, A. M., Memon, Z. A., Alhawat, M., Ali, M. & Milad, A. Estimation at completion in Civil engineering projects: Review of regression and soft computing models. Knowl. Based Eng. Sci. 2, 1–12 (2021).

    Google Scholar 

  • Tamura, S. & Tateishi, M. Capabilities of a four-layered feedforward neural network: Four layers versus three. IEEE Trans. Neural Netw. 8, 251–255 (1997).

    CAS 
    PubMed 

    Google Scholar 

  • Huang, G.-B. Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans. Neural Netw. 14, 274–281 (2003).

    PubMed 

    Google Scholar 

  • Ali, M., Deo, R. C., Downs, N. J. & Maraseni, T. Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting. Atmos. Res. 213, 450–464 (2018).

    Google Scholar 

  • Liang, N.-Y., Huang, G.-B., Saratchandran, P. & Sundararajan, N. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17, 1411–1423 (2006).

    PubMed 

    Google Scholar 

  • Lan, Y., Soh, Y. C. & Huang, G.-B. Ensemble of online sequential extreme learning machine. Neurocomputing 72, 3391–3395 (2009).

    Google Scholar 

  • Yadav, B., Ch, S., Mathur, S. & Adamowski, J. Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: A case study in Neckar River, Germany. Measurement 92, 433–445 (2016).

    ADS 

    Google Scholar 

  • Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).

    MATH 

    Google Scholar 

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    MATH 

    Google Scholar 

  • Al-Sulttani, A. O. et al. Proposition of new ensemble data-intelligence models for surface water quality prediction. IEEE Access 9, 108527–108541 (2021).

    Google Scholar 

  • Carranza, C., Nolet, C., Pezij, M. & Van Der Ploeg, M. Root zone soil moisture estimation with random forest. J. Hydrol. 593, 125840 (2021).

    Google Scholar 

  • Evans, J. S., Murphy, M. A., Holden, Z. A. & Cushman, S. A. Modeling species distribution and change using random forest. In Predictive Species and Habitat Modeling in Landscape Ecology (eds Ashton Drew, C. et al.) 139–159 (Springer, 2011).

    Google Scholar 

  • Rahmati, O., Pourghasemi, H. R. & Melesse, A. M. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. CATENA 137, 360–372 (2016).

    Google Scholar 

  • Prasad, R., Ali, M., Kwan, P. & Khan, H. Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. Appl. Energy 236, 778–792 (2019).

    Google Scholar 

  • Sharafati, A. et al. The potential of novel data mining models for global solar radiation prediction. Int. J. Environ. Sci. Technol. https://doi.org/10.1007/s13762-019-02344-0 (2019).

    Article 

    Google Scholar 

  • Service, A. M. I. District-Wise Area of Wheat Crop. Available at: http://www.amis.pk/Agristatistics/DistrictWise/2010-2012/Wheat.html (2012).

  • Service, A. M. I. District-Wise Area of Wheat Crop. Available at: http://www.amis.pk/Agristatistics/DistrictWise/2012-2014/Wheat.html (2014).

  • Punjab, P. Population. Available at: https://en.wikipedia.org/wiki/Punjab_Pakistan (2015).

  • Steiniger, S. & Hunter, A. J. S. The 2012 free and open source GIS software map—A guide to facilitate research, development, and adoption. Comput. Environ. Urban Syst. 39, 136–150 (2013).

    Google Scholar 

  • Hsu, C.-W. et al. A practical guide to support vector classification. BJU Int. https://doi.org/10.1177/02632760022050997 (2008).

    Article 
    PubMed 

    Google Scholar 

  • Bergmeir, C. & Benítez, J. M. On the use of cross-validation for time series predictor evaluation. Inf. Sci. (NY) 191, 192–213 (2012).

    Google Scholar 

  • Xia, Y., Liu, C., Li, Y. Y. & Liu, N. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst. Appl. https://doi.org/10.1016/j.eswa.2017.02.017 (2017).

    Article 

    Google Scholar 

  • Yen, B. C., ASCE Task Committee on Definition of Criteria for Evaluation of Watershed Models of the Watershed Management Committee Irrigation and Drainage Division. Discussion and closure: Criteria for evaluation of watershed models. J. Irrig. Drain. Eng. 121, 130–132 (1995).

    Google Scholar 

  • Yaseen, Z. M. An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere 277, 130126 (2021).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Dawson, C. W., Abrahart, R. J. & See, L. M. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ. Model. Softw. 22, 1034–1052 (2007).

    Google Scholar 

  • Legates, D. R. & Mccabe, G. J. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241 (1999).

    ADS 

    Google Scholar 

  • Willmott, C. J. & Willmott, C. J. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/1520-0477(1982)063%3c1309:SCOTEO%3e2.0.CO;2 (1982).

    Article 
    MATH 

    Google Scholar 

  • Willmott, C. J. On the validation of models. Phys. Geogr. https://doi.org/10.1080/02723646.1981.10642213 (1981).

    Article 
    MATH 

    Google Scholar 

  • Sharafati, A., Yasa, R. & Azamathulla, H. M. Assessment of stochastic approaches in prediction of wave-induced pipeline scour depth. J. Pipeline Syst. Eng. Pract. 9, 04018024 (2018).

    Google Scholar 

  • Mohammadi, K. et al. A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation. Energy Convers. Manag. 92, 162–171 (2015).

    Google Scholar 

  • Willmott, C. J., Robeson, S. M. & Matsuura, K. A refined index of model performance. Int. J. Climatol. 32, 2088–2094 (2012).

    Google Scholar 

  • Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 10, 282–290 (1970).

    ADS 

    Google Scholar 

  • Yaseen, Z. M. et al. Hourly river flow forecasting: Application of emotional neural network versus multiple machine learning paradigms. Water Resour. Manag. 34, 1075–1091 (2020).

    Google Scholar 

  • Bhagat, S. K., Tung, T. M. & Yaseen, Z. M. Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia. J. Hazard. Mater. 403, 123492 (2021).

    CAS 
    PubMed 

    Google Scholar 

  • Hora, J. & Campos, P. A review of performance criteria to validate simulation models. Expert Syst. 32, 578–595 (2015).

    Google Scholar 

  • Nourani, V., Kisi, Ö. & Komasi, M. Two hybrid Artificial Intelligence approaches for modeling rainfall-runoff process. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2011.03.002 (2011).

    Article 

    Google Scholar 

  • Ertekin, C. & Yaldiz, O. Comparison of some existing models for estimating global solar radiation for Antalya (Turkey). Energy Convers. Manag. 41, 311–330 (2000).

    Google Scholar 

  • Li, M. F., Tang, X. P., Wu, W. & Liu, H. B. General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Convers. Manag. 70, 139–148. https://doi.org/10.1016/j.enconman.2013.03.004 (2013).

    Article 

    Google Scholar 

  • Xu, Z., Hou, Z., Han, Y. & Guo, W. A diagram for evaluating multiple aspects of model performance in simulating vector fields. Geosci. Model Dev. 9, 4365–4380 (2016).

    ADS 

    Google Scholar 

  • Dan Foresee, F. & Hagan, M. T. Gauss–Newton approximation to bayesian learning. In IEEE International Conference on Neural Networks—Conference Proceedings. https://doi.org/10.1109/ICNN.1997.614194 (1997).

  • Akhtar, I. U. H. Pakistan needs a new crop forecasting system (2012).

  • Stathakis, D., Savina, I. & Nègrea, T. Neuro-fuzzy modeling for crop yield prediction. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 34, 1–4 (2006).

    Google Scholar 

  • Kumar, P., Gupta, D. K., Mishra, V. N. & Prasad, R. Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data. Int. J. Remote Sens. 36, 1604–1617 (2015).

    Google Scholar 

  • Sun, J., Xu, W. & Feng, B. A global search strategy of quantum-behaved particle swarm optimization. In 2004 IEEE Conference on Cybernetics and Intelligent Systems. https://doi.org/10.1109/iccis.2004.1460396 (2004)

  • Naganna, S. et al. Dew point temperature estimation: Application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water. https://doi.org/10.3390/w11040742 (2019).

    Article 

    Google Scholar 

  • Gilles, J. Empirical wavelet transform. IEEE Trans. Signal Process. 61, 3999–4010 (2013).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Bokde, N., Feijóo, A., Al-Ansari, N., Tao, S. & Yaseen, Z. M. The hybridization of ensemble empirical mode decomposition with forecasting models: Application of short-term wind speed and power modeling. Energies 13, 1666 (2020).

    Google Scholar 

  • Chau, K. W. & Wu, C. L. A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J. Hydroinform. 12, 458–473 (2010).

    Google Scholar 


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