Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Theory of Extreme Learning Machine (ELM)
2.3. Description of Selected Descriptors and Data Analysis
2.4. Model Validation
2.5. Software
3. Results and Discussion
3.1. Impact Analysis of Descriptors
3.1.1. Temperature
3.1.2. Precipitation
3.1.3. Water Deficit
3.1.4. Sunshine
3.1.5. Humidity
3.2. Extreme Learning Machine (ELM) Model Establishment
3.3. The Application Domain (AD) of ELM Models
3.4. Prediction of Maize Yield under Various Climate Factors
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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No. | Parameters | t | Sig. | |
---|---|---|---|---|
Climate Factors | Month | |||
1 | Water deficit | 05 | 1.279 | 0.203 |
2 | Water deficit | 06 | −0.322 | 0.748 |
3 | Water deficit | 07 | −2.118 | 0.036 |
4 | Water deficit | 08 | −0.443 | 0.658 |
5 | Temp. Mean | 05 | 0.700 | 0.485 |
6 | Temp. Mean | 06 | −0.659 | 0.511 |
7 | Temp. Mean | 07 | 0.110 | 0.912 |
8 | Temp. Mean | 08 | 1.020 | 0.310 |
9 | Temp. Max | 05 | −1.726 | 0.087 |
10 | Temp. Max | 06 | 1.356 | 0.177 |
11 | Temp. Max | 07 | 0.039 | 0.969 |
12 | Temp. Max | 08 | −0.963 | 0.337 |
13 | Temp. Min | 05 | 1.039 | 0.301 |
14 | Temp. Min | 06 | −0.868 | 0.387 |
15 | Temp. Min | 07 | 1.639 | 0.104 |
16 | Temp. Min | 08 | −1.261 | 0.210 |
17 | Humidity | 05 | 1.007 | 0.316 |
18 | Humidity | 06 | −1.108 | 0.270 |
19 | Humidity | 07 | −0.476 | 0.635 |
20 | Humidity | 08 | 0.266 | 0.791 |
21 | Precipitation | 05 | −0.135 | 0.893 |
22 | Precipitation | 06 | 0.595 | 0.553 |
23 | Precipitation | 07 | −0.768 | 0.444 |
24 | Precipitation | 08 | 0.186 | 0.853 |
25 | Sunshine | 05 | 2.158 | 0.033 |
26 | Sunshine | 06 | −2.955 | 0.004 |
27 | Sunshine | 07 | −2.194 | 0.030 |
28 | Sunshine | 08 | 0.744 | 0.458 |
29 | (Constant) | 3.239 | 0.002 |
No. | Parameters | t | Sig. | |
---|---|---|---|---|
Climate Factors | Month | |||
1 | Water deficit | 05 | 2.971 | 0.004 |
2 | Water deficit | 06 | 0.834 | 0.406 |
3 | Water deficit | 07 | −0.958 | 0.340 |
4 | Water deficit | 08 | −0.308 | 0.759 |
5 | Water deficit | 09 | −0.327 | 0.744 |
6 | Temp. Mean | 05 | 0.603 | 0.548 |
7 | Temp. Mean | 06 | −0.505 | 0.614 |
8 | Temp. Mean | 07 | 0.654 | 0.514 |
9 | Temp. Mean | 08 | 1.405 | 0.163 |
10 | Temp. Mean | 09 | 1.185 | 0.238 |
11 | Temp. Max | 05 | 1.458 | 0.148 |
12 | Temp. Max | 06 | 0.314 | 0.754 |
13 | Temp. Max | 07 | −1.263 | 0.209 |
14 | Temp. Max | 08 | −0.355 | 0.723 |
15 | Temp. Max | 09 | −1.622 | 0.108 |
16 | Temp. Min | 05 | −1.609 | 0.110 |
17 | Temp. Min | 06 | 0.134 | 0.894 |
18 | Temp. Min | 07 | 1.610 | 0.110 |
19 | Temp. Min | 08 | −0.739 | 0.462 |
20 | Temp. Min | 09 | 3.134 | 0.002 |
21 | Humidity | 05 | 1.334 | 0.185 |
22 | Humidity | 06 | 1.641 | 0.103 |
23 | Humidity | 07 | 1.456 | 0.148 |
24 | Humidity | 08 | −0.281 | 0.779 |
25 | Humidity | 09 | −1.059 | 0.292 |
26 | Precipitation | 05 | 2.074 | 0.040 |
27 | Precipitation | 06 | −0.860 | 0.392 |
28 | Precipitation | 07 | −0.341 | 0.734 |
29 | Precipitation | 08 | 0.578 | 0.565 |
30 | Precipitation | 09 | −0.973 | 0.333 |
31 | Sunshine | 05 | −1.433 | 0.154 |
32 | Sunshine | 06 | 1.525 | 0.130 |
33 | Sunshine | 07 | −0.633 | 0.528 |
34 | Sunshine | 08 | −1.909 | 0.059 |
35 | Sunshine | 09 | 0.179 | 0.858 |
36 | (Constant) | −1.507 | 0.134 |
No. | Parameters | t | Sig. | |
---|---|---|---|---|
Climate Factors | Month | |||
1 | Water deficit | 05 | 2.643 | 0.009 |
2 | Water deficit | 06 | 0.686 | 0.494 |
3 | Water deficit | 07 | −0.509 | 0.612 |
4 | Water deficit | 08 | −0.153 | 0.879 |
5 | Water deficit | 09 | 0.485 | 0.628 |
6 | Temp. Mean | 05 | 0.002 | 0.999 |
7 | Temp. Mean | 06 | −0.186 | 0.853 |
8 | Temp. Mean | 07 | 1.107 | 0.271 |
9 | Temp. Mean | 08 | 1.804 | 0.074 |
10 | Temp. Mean | 09 | 0.293 | 0.770 |
11 | Temp. Max | 05 | 1.934 | 0.056 |
12 | Temp. Max | 06 | −0.126 | 0.900 |
13 | Temp. Max | 07 | −1.923 | 0.057 |
14 | Temp. Max | 08 | −0.090 | 0.929 |
15 | Temp. Max | 09 | −1.393 | 0.166 |
16 | Temp. Min | 05 | −1.360 | 0.177 |
17 | Temp. Min | 06 | −0.195 | 0.846 |
18 | Temp. Min | 07 | 1.718 | 0.089 |
19 | Temp. Min | 08 | −0.753 | 0.453 |
20 | Temp. Min | 09 | 3.439 | 0.001 |
21 | Humidity | 05 | 0.320 | 0.749 |
22 | Humidity | 06 | 2.033 | 0.044 |
23 | Humidity | 07 | 0.655 | 0.514 |
24 | Humidity | 08 | −0.273 | 0.785 |
25 | Humidity | 09 | −0.310 | 0.757 |
26 | Precipitation | 05 | 1.784 | 0.077 |
27 | Precipitation | 06 | −1.095 | 0.276 |
28 | Precipitation | 07 | −0.173 | 0.863 |
29 | Precipitation | 08 | 0.517 | 0.606 |
30 | Precipitation | 09 | −0.895 | 0.373 |
31 | Sunshine | 05 | −2.216 | 0.029 |
32 | Sunshine | 06 | 2.069 | 0.041 |
33 | Sunshine | 07 | −1.029 | 0.306 |
34 | Sunshine | 08 | −2.517 | 0.013 |
35 | Sunshine | 09 | 0.234 | 0.815 |
36 | (Constant) | −0.817 | 0.416 |
No. | Models | Datasets | Number of Datasets | AARD % | RMSE | R2 |
---|---|---|---|---|---|---|
1 | ELM1 | Train | 123 | 6.200 | 2.675 | 0.674 |
test | 30 | 8.062 | 3.307 | 0.560 | ||
total | 153 | 6.565 | 2.810 | 0.641 | ||
2 | ELM2 | Train | 122 | 35.791 | 1.052 | 0.746 |
test | 30 | 17.335 | 1.145 | 0.754 | ||
total | 152 | 32.148 | 1.071 | 0.741 | ||
3 | ELM3 | Train | 117 | 15.126 | 1.013 | 0.705 |
test | 29 | 15.193 | 0.981 | 0.773 | ||
total | 146 | 15.139 | 1.006 | 0.716 |
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Maitah, M.; Malec, K.; Ge, Y.; Gebeltová, Z.; Smutka, L.; Blažek, V.; Pánková, L.; Maitah, K.; Mach, J. Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia. Agronomy 2021, 11, 2344. https://doi.org/10.3390/agronomy11112344
Maitah M, Malec K, Ge Y, Gebeltová Z, Smutka L, Blažek V, Pánková L, Maitah K, Mach J. Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia. Agronomy. 2021; 11(11):2344. https://doi.org/10.3390/agronomy11112344
Chicago/Turabian StyleMaitah, Mansoor, Karel Malec, Ying Ge, Zdeňka Gebeltová, Luboš Smutka, Vojtěch Blažek, Ludmila Pánková, Kamil Maitah, and Jiří Mach. 2021. "Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia" Agronomy 11, no. 11: 2344. https://doi.org/10.3390/agronomy11112344