# Archaeological Predictive Modeling Using Machine Learning and Statistical Methods for Japan and China

^{1}

^{2}

^{3}

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## Abstract

**:**

## 1. Introduction

## 2. Materials

#### 2.1. Study Area and Archaeological Background

^{2}. Approximately three-quarters of the country’s terrain is mountainous [19]. Kyoto and Nara are representative historical capitals, home to thousands of archaeological sites (monuments, rituals, burial sites, etc.) [20]. The Japanese archaeological sites considered in this study are burial tombs built in the Kofun period, from the 3rd to the 6th centuries CE [21] or between the middle of the 3rd and the early 7th centuries [22]. The Kofun era is named for the large earthen tombs that characterized and defined the period. The typical tomb type is called “Zempō-kōen fun” in Japanese from its characteristic shape: a square protrusion connects to a main circular hill, forming a keyhole shape [22,23]. Hence, the name “Keyhole-shaped tomb” is used in some related studies [24,25]. The scale of the tombs ranges from several meters to more than 400 m [26,27,28]; the tomb’s size is thought to represent the buried person’s power and status [29,30]. In addition, this keyhole-shaped tomb is unique to Japan [31].

^{2}. Approximately 45% of the area consists of plateaus, followed by mountains (36%) and plains (18%) [31]. The province is an important cradleland of the Chinese nation. For nearly 2000 years, from the establishment of the Western Zhou Dynasty to the fall of the Tang Dynasty, 14 successive dynasties established their capitals there [32]. Particularly notable is Xi’an, the current capital city of the province with a long and rich cultural history, where most previous dynasties established their capitals [33]. Moreover, the Silk Road, the historical network of Eurasian trade routes, originated in this region [34]. The Mausoleum of Qin Shihuang in Shaanxi Province is the largest tomb in Chinese history. Xi’an also has the Terracotta Warriors and Horses in a mausoleum, China’s earliest World Heritage Site. There are many ground-level and underground ruins near the mausoleum [35]. Because of its diverse cultural history, the Shaanxi dataset contains a variety of archaeological monuments, such as tombs, temples, and palaces, spanning over 2000 years (1046 BC–907 AD).

#### 2.2. Archaeological Data

#### 2.3. Geomorphological Predictive Factors

## 3. Methodology

#### 3.1. Selection of Predictive Factors

#### 3.2. Frequency Ratio (FR)

#### 3.3. Hybrid Model of Attention Mechanism and Frequency Ratio (AM_FR)

#### 3.3.1. Problem Formulation

#### 3.3.2. Model Architecture

#### 3.3.3. Implementation

#### 3.4. MaxEnt

#### 3.5. Model Evaluation

## 4. Results

#### 4.1. Selected Predictive Factors

#### 4.2. APM by FR

#### 4.3. APM by the AM_FR Model

_{FR}+ 0.0714 Slope

_{FR}+ 0.0997Roughness

_{FR}+

0.3356RDLS

_{FR}+ 0.0424Plan_Curvature

_{FR}+ 0.0717Profile_Curvature

_{FR}+

0.2813Cutting_depth

_{FR}+ 0.0834Distance from major rivers

_{FR}

_{FR}+ 0.2878 Slope

_{FR}+ 0.0948Roughness

_{FR}+

0.1524RDLS

_{FR }+ 0.0809Plan_Curvature

_{FR}+ 0.0916Profile_Curvature

_{FR}+

0.0869Cutting_depth

_{FR}+ 0.0833 Distance from major rivers

_{FR}

#### 4.4. APM by MaxEnt

#### 4.5. Model Evaluation

#### 4.6. Archaeological Predictive Maps and Statistics

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Maps of topographic factors for Japan: (

**a**) elevation; (

**b**) slope; (

**c**) roughness; (

**d**) RDLS; (

**e**) plan curvature; (

**f**) profile curvature; (

**g**) cutting depth.

**Figure 3.**Maps of topographic factors for Shaanxi, China: (

**a**) elevation; (

**b**) slope; (

**c**) roughness; (

**d**) RDLS; (

**e**) plan curvature; (

**f**) profile curvature; (

**g**) cutting depth.

**Figure 5.**Overview of our model’s architecture. First, one-hot embedding [57,58] is used to embed each topographic factor. Then, we utilize a conditional attention mechanism to learn the different weights of the input factors in relation to archaeological sites and estimate archaeological site locations. The whole model is trained end-to-end.

**Figure 6.**Statistical results of classification and FR of predictors for Japan: (

**a**) elevation; (

**b**) slope; (

**c**) roughness; (

**d**) RDLS; (

**e**) plan curvature; (

**f**) profile curvature; (

**g**) cutting depth; (

**h**) distance from major rivers.

**Figure 7.**Statistical results of classification and FR of predictors for Shaanxi, China: (

**a**) elevation; (

**b**) slope; (

**c**) roughness; (

**d**) RDLS; (

**e**) plan curvature; (

**f**) profile curvature; (

**g**) cutting depth; (

**h**) distance from major rivers.

**Figure 10.**Results of zonal statistics for the main correlated factors in Japan: (

**a**) RDLS; (

**b**) cutting depth; (

**c**) roughness.

**Figure 11.**Results of zonal statistics for the main correlated factors in Shaanxi, China: (

**a**) slope; (

**b**) RDLS; (

**c**) elevation.

Factor | Japan | Shaanxi, China | ||
---|---|---|---|---|

r | p | r | p | |

Elevation | −0.512 * | 0.000 | −0.603 * | 0.000 |

Slope | −0.532 * | 0.000 | −0.480 * | 0.000 |

Roughness | −0.407 * | 0.000 | −0.408 * | 0.000 |

RDLS | −0.611 * | 0.000 | −0.478 * | 0.000 |

Plan curvature | 0.189 * | 0.000 | 0.144 * | 0.004 |

Profile curvature | −0.218 * | 0.000 | −0.167 * | 0.001 |

Cutting depth | −0.585 * | 0.000 | −0.453 * | 0.000 |

Aspect | 0.021 | 0.277 | −0.009 | 0.858 |

Distance from major rivers | −0.320 * | 0.000 | −0.239 * | 0.000 |

Elevation variation | 0.215 * | 0.000 | −0.129 * | 0.000 |

**Table 2.**Multicollinearity results for predictors in the pretest (1st run) and the posttest (2nd run).

Factor | Japan | Shaanxi, China | ||
---|---|---|---|---|

VIF (1st/2nd) | TOL (1st/2nd) | VIF (1st/2nd) | TOL (1st/2nd) | |

Elevation | 1.736/1.663 | 0.576/0.601 | 3.316/2.683 | 0.302/0.372 |

Elevation Variation | 20.927/– | 0.048/– | 25.497/– | 0.039/– |

Slope | 19.403/8.019 | 0.052/0.125 | 18.980/9.489 | 0.053/0.105 |

Roughness | 5.295/5.163 | 0.189/0.194 | 5.906/5.722 | 0.169/0.175 |

RDLS | 6.980/5.633 | 0.143/0.178 | 10.972/8.520 | 0.091/0.117 |

Plan curvature | 1.510/1.484 | 0.662/0.674 | 1.830/1.606 | 0.546/0.623 |

Profile curvature | 1.547/1.487 | 0.646/0.672 | 2.525/2.052 | 0.396/0.487 |

Cutting depth | 6.604/5.222 | 0.151/0.191 | 8.512/7.981 | 0.123/0.125 |

Distance from major rivers | 1.315/1.298 | 0.761/0.770 | 1.726/1.656 | 0.580/0.604 |

Factor | Weight | |
---|---|---|

Japan | Shaanxi, China | |

Elevation | 0.0145 | 0.1223 |

Slope | 0.0714 | 0.2878 |

Roughness | 0.0997 | 0.0948 |

RDLS | 0.3356 | 0.1524 |

Plan curvature | 0.0424 | 0.0809 |

Profile curvature | 0.0717 | 0.0916 |

Cutting depth | 0.2813 | 0.0869 |

Distance from major rivers | 0.0834 | 0.0833 |

Total | 1.0000 | 1.0000 |

Factor | Contribution (%) | |
---|---|---|

Japan | Shaanxi, China | |

Elevation | 8.7 | 25.7 |

Slope | 3.0 | 23.8 |

Roughness | 33.7 | 16.2 |

RDLS | 24.3 | 18.5 |

Plan curvature | 1.2 | 3.7 |

Profile curvature | 2.3 | 1.2 |

Cutting depth | 16.5 | 2.7 |

Distance from major rivers | 10.3 | 8.2 |

Study Area | K-Fold | Training AUC | Test AUC | ||||
---|---|---|---|---|---|---|---|

AM_FR | Maxent | FR | AM_FR | Maxent | FR | ||

Japan | 1 | 0.903 | 0.875 | 0.841 | 0.909 | 0.876 | 0.868 |

2 | 0.896 | 0.874 | 0.845 | 0.895 | 0.873 | 0.835 | |

3 | 0.902 | 0.876 | 0.862 | 0.897 | 0.865 | 0.864 | |

4 | 0.898 | 0.876 | 0.854 | 0.901 | 0.874 | 0.856 | |

Mean | 0.900 | 0.876 | 0.851 | 0.901 | 0.872 | 0.856 | |

Shaanxi, China | 1 | 0.789 | 0.782 | 0.776 | 0.813 | 0.780 | 0.781 |

2 | 0.809 | 0.805 | 0.763 | 0.826 | 0.803 | 0.812 | |

3 | 0.767 | 0.795 | 0.754 | 0.814 | 0.783 | 0.798 | |

4 | 0.768 | 0.791 | 0.763 | 0.812 | 0.773 | 0.772 | |

Mean | 0.783 | 0.793 | 0.764 | 0.816 | 0.785 | 0.791 |

Model | Class | Japan | Shaanxi, China | ||||||
---|---|---|---|---|---|---|---|---|---|

Area (%) | Site | Site (%) | Kvamme’s Gain | Area (%) | Site | Site (%) | Kvamme’s Gain | ||

AM_FR | Very Low | 42.67% | 29 | 2.12% | −19.11 | 35.14% | 2 | 1.00% | −34.14 |

Low | 19.12% | 61 | 4.46% | −3.29 | 28.11% | 21 | 10.50% | −1.68 | |

Moderate | 11.20% | 170 | 12.44% | 0.10 | 13.16% | 23 | 11.50% | −0.14 | |

High | 18.09% | 542 | 39.65% | 0.54 | 15.91% | 55 | 27.50% | 0.42 | |

Very High | 8.92% | 565 | 41.33% | 0.78 | 7.68% | 99 | 49.50% | 0.84 | |

Maxent | Very Low | 70.07% | 82 | 6.00% | −10.68 | 72.10% | 23 | 11.50% | −5.27 |

Low | 11.89% | 138 | 10.10% | −0.18 | 15.66% | 19 | 9.50% | −0.65 | |

Moderate | 8.77% | 281 | 20.56% | 0.57 | 2.05% | 21 | 10.50% | 0.80 | |

High | 6.46% | 413 | 30.21% | 0.79 | 4.87% | 42 | 21.00% | 0.77 | |

Very High | 2.81% | 453 | 33.14% | 0.92 | 5.32% | 95 | 47.50% | 0.89 | |

FR | Very Low | 31.62% | 36 | 2.63% | −11.01 | 23.79% | 7 | 3.50% | −5.80 |

Low | 24.04% | 98 | 7.17% | −2.35 | 36.35% | 23 | 11.50% | −2.16 | |

Moderate | 10.77% | 117 | 8.56% | −0.26 | 15.54% | 35 | 17.50% | 0.11 | |

High | 19.37% | 433 | 31.68% | 0.39 | 17.06% | 48 | 24.00% | 0.29 | |

Very High | 14.20% | 683 | 49.96% | 0.72 | 7.25% | 87 | 43.50% | 0.83 |

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## Share and Cite

**MDPI and ACS Style**

Wang, Y.; Shi, X.; Oguchi, T.
Archaeological Predictive Modeling Using Machine Learning and Statistical Methods for Japan and China. *ISPRS Int. J. Geo-Inf.* **2023**, *12*, 238.
https://doi.org/10.3390/ijgi12060238

**AMA Style**

Wang Y, Shi X, Oguchi T.
Archaeological Predictive Modeling Using Machine Learning and Statistical Methods for Japan and China. *ISPRS International Journal of Geo-Information*. 2023; 12(6):238.
https://doi.org/10.3390/ijgi12060238

**Chicago/Turabian Style**

Wang, Yuan, Xiaodan Shi, and Takashi Oguchi.
2023. "Archaeological Predictive Modeling Using Machine Learning and Statistical Methods for Japan and China" *ISPRS International Journal of Geo-Information* 12, no. 6: 238.
https://doi.org/10.3390/ijgi12060238