第五章 結論與建議
5.2 建議
5.2.1 HMM 土壤分層法
1. 關聯性長度 δ 為唯一一個沒有被我們納入 Gibbs sampling 估算的隨機場參 數，這是因為δ 並沒有共軛先驗的條件可以求出 Gibbs sampling 所需的後驗分布，
必須採用其他 MCMC 方法，然而我們測試過 Metropolis-Hastings 演算法、矩估計 法 (method of moments)，表現皆不夠理想，仍未找到能將 δ 估得精準的方法，若 之後的研究者能找到適合估算δ 的方法，HMM 法將能考慮 δ 的改變，使土壤分層 更具靈敏度。
2. 孤兒層問題導因於目標分群之標準差太小，過渡區的資料點無法被分類到 該群，因此被歸類到標準差較大的分群，產生一個不合理的薄層，若後續研究能 判斷這些資料點應該被分類到哪些族群，並防止它們被歸類到其他不合理的族群，
同時又不破壞 HMM 的架構，將能阻止孤兒層問題的發生。實際上，小於關聯性 長度δ 的薄層其獨立性本身將受到質疑，這是因為隨機場理論中當兩點距離大於 δ 才能視為它們之間沒有關聯性，小於δ 的薄層將不符合我們「不同土壤種類之間 互相獨立」的這個假設，後續研究若能防止產生小於δ 的薄層，同時防止孤兒層 問題的發生，將能使 HMM 土壤分層結果更符合我們的假設，得到更清楚的分層。
3. 「分層分數問題」使得分層分數變得不是絕對可信，實際上我們還是沒有 得到一個最佳分層數的指標，若後續研究能防止預燒期問題在分群數高時產生多 種收斂結果的問題，使 HMM 後期抓取的層數皆相同，則分群分數在每一個群數
將會只有一種分數，讓使用者更容易找出最佳分群數。
5.2.2 多維土壤剖面預測
1. 目前 WTMM 四分法判定土層位置與土壤種類之後，GCMC 模型便會完全 根據輸入的土壤種類進行多維分層預測，然而 WTMM 四分法所判斷的土壤種類是 機率性的，某層土壤根據 SBTn 圖的劃分，可能會顯示其具有 60 %的機率砂土，
40 %的機率是粉土，因此 WTMM 四分法最終會判定其為砂土，結果 GCMC 會是 以 100 %相信它是砂土的條件下進行分析，忽略了 40 %機率是粉土的影響，造成 分析結果會與實際情況有所出入，因此往後多維土壤分層模型若是能同時考慮不 同土壤種類機率的影響的話，也就是以圖 4-14(c)為輸入資料，而不是圖 4-14(b)，
或許將能更準確地預測土層的分布情形。
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