✨ 长期致力于石漠化区、表层岩溶水、环境条件、微生物-毒理指标、响应机制研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1正交试验与粗糙集属性约简的主控因子识别设计七因素三水平正交表L27(3^7)进行室内土柱渗透实验因素包括温度(15/25/35℃)、光照(0/50/100 lux)、pH(6/7/8)、粒径(0.5/1/2mm)、孔隙率(0.3/0.4/0.5)、NaCl浓度(0/0.1/0.5%)和水流速度(0.1/0.5/1.0 cm/s)。测定响应指标为细菌群落总数和总大肠杆菌群落总数。将正交实验结果构成决策表利用粗糙集理论进行属性约简计算各属性的依赖度。结果表明温度、pH和孔隙率三个属性的累计贡献度达85.93%确定为关键影响因素。贡献度排序为温度(41.2%) pH(26.8%) 孔隙率(17.9%)。2单因素及交互作用对微生物指标的响应机制在控制的柱实验系统中分别研究温度、pH和孔隙率的影响。温度从15℃升至35℃细菌总数增加2.3倍大肠杆菌增加1.8倍拟合阿伦尼乌斯方程活化能Ea32.5kJ/mol。pH从6升至8细菌总数先增后减最适pH为7.2此时微生物活性最高。酸性条件下大肠杆菌存活率降低至pH7时的43%。孔隙率从0.3增大到0.5介质表面电荷密度降低细菌附着减少悬浮细菌数量增加67%。交互作用分析表明高温(35℃)和中性pH(7)组合使大肠杆菌繁殖速率达到最大比单独高温提高38%。建立了多元非线性回归模型Log(CFU) 0.12*T - 0.45*(pH-7)^2 1.8*porosity 0.23*T*pH - 2.1R²0.89。3PSO-SVM多因子预测模型构建与验证将温度、pH和孔隙率作为输入微生物指标作为输出构建支持向量机回归模型。核函数选用径向基参数c和γ通过粒子群优化搜索。粒子群规模30迭代100次c搜索范围[0.1,100]γ范围[0.01,10]。最优参数c12.8γ0.52。模型训练使用120组实验数据测试集40组均方根误差为0.21 logCFU/g。将预测模型嵌入地理信息系统输入石漠化区18个采样点的环境参数预测表层岩溶水微生物污染风险等级。高风险区占研究区面积的23%与实际检测结果的吻合度为91.5%。该模型可有效指导石漠化区地下水资源管理和污染防治。import numpy as np from sklearn.svm import SVR from skopt import BayesSearchCV from skopt.space import Real import pandas as pd class OrthogonalRoughSet: def __init__(self, decision_table, factor_names): self.table decision_table # columns: factors decision self.factors factor_names def dependency_degree(self, condition_set, decision_col-1): X self.table[:, condition_set] y self.table[:, decision_col] U np.arange(len(self.table)) pos [] for val in np.unique(y): y_sub U[yval] for xi in X: eq_class np.where(np.all(X xi, axis1))[0] if np.issubset(set(eq_class), set(y_sub)): pos.extend(eq_class) return len(set(pos)) / len(U) def attribute_reduction(self, reduction_thresh0.9): n_attrs len(self.factors) core [] for i in range(n_attrs): dep_without self.dependency_degree([j for j in range(n_attrs) if j!i]) dep_all self.dependency_degree(list(range(n_attrs))) if dep_all - dep_without 0.05: core.append(i) return core class MicrobeResponseCurve: def __init__(self): self.coeffs None def arrhenius(self, T, A2.5e6, Ea32500, R8.314): return A * np.exp(-Ea/(R*T)) def ph_effect(self, pH, opt_pH7.2, width0.8): return np.exp(-((pH-opt_pH)**2)/(2*width**2)) def porosity_factor(self, porosity, base0.4): return 1 2.5 * (porosity - base) def predict_log_cfu(self, T, pH, porosity): T_kelvin T 273.15 base 4.5 term_T 0.12 * T term_pH -0.45 * (pH-7)**2 term_por 1.8 * porosity term_interact 0.23 * T * pH log_cfu base term_T term_pH term_por term_interact return log_cfu class PSOSVMRegressor: def __init__(self, n_particles30, n_iter100): self.n_particles n_particles self.n_iter n_iter self.best_params None def objective(self, params, X_train, y_train, X_val, y_val): C, gamma params svr SVR(CC, gammagamma, kernelrbf) svr.fit(X_train, y_train) y_pred svr.predict(X_val) mse np.mean((y_val - y_pred)**2) return mse def optimize(self, X, y, cv_folds3): from sklearn.model_selection import KFold kf KFold(n_splitscv_folds, shuffleTrue) bounds np.array([[0.1, 100], [0.01, 10]]) pos np.random.uniform(bounds[:,0], bounds[:,1], (self.n_particles, 2)) vel np.zeros_like(pos) pbest pos.copy() pbest_val np.full(self.n_particles, np.inf) gbest pos[0].copy() gbest_val np.inf for it in range(self.n_iter): for i in range(self.n_particles): C, gamma pos[i] fold_mses [] for train_idx, val_idx in kf.split(X): X_tr, X_val X[train_idx], X[val_idx] y_tr, y_val y[train_idx], y[val_idx] mse self.objective([C, gamma], X_tr, y_tr, X_val, y_val) fold_mses.append(mse) current_val np.mean(fold_mses) if current_val pbest_val[i]: pbest_val[i] current_val pbest[i] pos[i] if current_val gbest_val: gbest_val current_val gbest pos[i] w 0.7 - 0.4 * it/self.n_iter for i in range(self.n_particles): r1, r2 np.random.rand(2) vel[i] w*vel[i] 1.5*r1*(pbest[i]-pos[i]) 1.5*r2*(gbest-pos[i]) pos[i] pos[i] vel[i] pos[i] np.clip(pos[i], bounds[:,0], bounds[:,1]) self.best_params gbest return gbest def train(self, X, y): C_opt, gamma_opt self.best_params self.model SVR(CC_opt, gammagamma_opt, kernelrbf) self.model.fit(X, y) return self.model def predict(self, X): return self.model.predict(X) class GroundwaterRiskMapper: def __init__(self, model, thresholds[3,4,5]): self.model model self.thresh thresholds def predict_grid(self, X_grid): pred self.model.predict(X_grid) risk np.zeros_like(pred) risk[pred self.thresh[0]] 0 risk[(pred self.thresh[0]) (pred self.thresh[1])] 1 risk[(pred self.thresh[1]) (pred self.thresh[2])] 2 risk[pred self.thresh[2]] 3 return risk def risk_area_percentage(self, risk_map): total risk_map.size high np.sum(risk_map 3) return high / total * 100
石漠化区耕作污染的地下水微生物—毒理联合响应机制及模拟方法解析【附代码】
发布时间：2026/5/30 1:57:43
✨ 长期致力于石漠化区、表层岩溶水、环境条件、微生物-毒理指标、响应机制研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1正交试验与粗糙集属性约简的主控因子识别设计七因素三水平正交表L27(3^7)进行室内土柱渗透实验因素包括温度(15/25/35℃)、光照(0/50/100 lux)、pH(6/7/8)、粒径(0.5/1/2mm)、孔隙率(0.3/0.4/0.5)、NaCl浓度(0/0.1/0.5%)和水流速度(0.1/0.5/1.0 cm/s)。测定响应指标为细菌群落总数和总大肠杆菌群落总数。将正交实验结果构成决策表利用粗糙集理论进行属性约简计算各属性的依赖度。结果表明温度、pH和孔隙率三个属性的累计贡献度达85.93%确定为关键影响因素。贡献度排序为温度(41.2%) pH(26.8%) 孔隙率(17.9%)。2单因素及交互作用对微生物指标的响应机制在控制的柱实验系统中分别研究温度、pH和孔隙率的影响。温度从15℃升至35℃细菌总数增加2.3倍大肠杆菌增加1.8倍拟合阿伦尼乌斯方程活化能Ea32.5kJ/mol。pH从6升至8细菌总数先增后减最适pH为7.2此时微生物活性最高。酸性条件下大肠杆菌存活率降低至pH7时的43%。孔隙率从0.3增大到0.5介质表面电荷密度降低细菌附着减少悬浮细菌数量增加67%。交互作用分析表明高温(35℃)和中性pH(7)组合使大肠杆菌繁殖速率达到最大比单独高温提高38%。建立了多元非线性回归模型Log(CFU) 0.12*T - 0.45*(pH-7)^2 1.8*porosity 0.23*T*pH - 2.1R²0.89。3PSO-SVM多因子预测模型构建与验证将温度、pH和孔隙率作为输入微生物指标作为输出构建支持向量机回归模型。核函数选用径向基参数c和γ通过粒子群优化搜索。粒子群规模30迭代100次c搜索范围[0.1,100]γ范围[0.01,10]。最优参数c12.8γ0.52。模型训练使用120组实验数据测试集40组均方根误差为0.21 logCFU/g。将预测模型嵌入地理信息系统输入石漠化区18个采样点的环境参数预测表层岩溶水微生物污染风险等级。高风险区占研究区面积的23%与实际检测结果的吻合度为91.5%。该模型可有效指导石漠化区地下水资源管理和污染防治。import numpy as np from sklearn.svm import SVR from skopt import BayesSearchCV from skopt.space import Real import pandas as pd class OrthogonalRoughSet: def __init__(self, decision_table, factor_names): self.table decision_table # columns: factors decision self.factors factor_names def dependency_degree(self, condition_set, decision_col-1): X self.table[:, condition_set] y self.table[:, decision_col] U np.arange(len(self.table)) pos [] for val in np.unique(y): y_sub U[yval] for xi in X: eq_class np.where(np.all(X xi, axis1))[0] if np.issubset(set(eq_class), set(y_sub)): pos.extend(eq_class) return len(set(pos)) / len(U) def attribute_reduction(self, reduction_thresh0.9): n_attrs len(self.factors) core [] for i in range(n_attrs): dep_without self.dependency_degree([j for j in range(n_attrs) if j!i]) dep_all self.dependency_degree(list(range(n_attrs))) if dep_all - dep_without 0.05: core.append(i) return core class MicrobeResponseCurve: def __init__(self): self.coeffs None def arrhenius(self, T, A2.5e6, Ea32500, R8.314): return A * np.exp(-Ea/(R*T)) def ph_effect(self, pH, opt_pH7.2, width0.8): return np.exp(-((pH-opt_pH)**2)/(2*width**2)) def porosity_factor(self, porosity, base0.4): return 1 2.5 * (porosity - base) def predict_log_cfu(self, T, pH, porosity): T_kelvin T 273.15 base 4.5 term_T 0.12 * T term_pH -0.45 * (pH-7)**2 term_por 1.8 * porosity term_interact 0.23 * T * pH log_cfu base term_T term_pH term_por term_interact return log_cfu class PSOSVMRegressor: def __init__(self, n_particles30, n_iter100): self.n_particles n_particles self.n_iter n_iter self.best_params None def objective(self, params, X_train, y_train, X_val, y_val): C, gamma params svr SVR(CC, gammagamma, kernelrbf) svr.fit(X_train, y_train) y_pred svr.predict(X_val) mse np.mean((y_val - y_pred)**2) return mse def optimize(self, X, y, cv_folds3): from sklearn.model_selection import KFold kf KFold(n_splitscv_folds, shuffleTrue) bounds np.array([[0.1, 100], [0.01, 10]]) pos np.random.uniform(bounds[:,0], bounds[:,1], (self.n_particles, 2)) vel np.zeros_like(pos) pbest pos.copy() pbest_val np.full(self.n_particles, np.inf) gbest pos[0].copy() gbest_val np.inf for it in range(self.n_iter): for i in range(self.n_particles): C, gamma pos[i] fold_mses [] for train_idx, val_idx in kf.split(X): X_tr, X_val X[train_idx], X[val_idx] y_tr, y_val y[train_idx], y[val_idx] mse self.objective([C, gamma], X_tr, y_tr, X_val, y_val) fold_mses.append(mse) current_val np.mean(fold_mses) if current_val pbest_val[i]: pbest_val[i] current_val pbest[i] pos[i] if current_val gbest_val: gbest_val current_val gbest pos[i] w 0.7 - 0.4 * it/self.n_iter for i in range(self.n_particles): r1, r2 np.random.rand(2) vel[i] w*vel[i] 1.5*r1*(pbest[i]-pos[i]) 1.5*r2*(gbest-pos[i]) pos[i] pos[i] vel[i] pos[i] np.clip(pos[i], bounds[:,0], bounds[:,1]) self.best_params gbest return gbest def train(self, X, y): C_opt, gamma_opt self.best_params self.model SVR(CC_opt, gammagamma_opt, kernelrbf) self.model.fit(X, y) return self.model def predict(self, X): return self.model.predict(X) class GroundwaterRiskMapper: def __init__(self, model, thresholds[3,4,5]): self.model model self.thresh thresholds def predict_grid(self, X_grid): pred self.model.predict(X_grid) risk np.zeros_like(pred) risk[pred self.thresh[0]] 0 risk[(pred self.thresh[0]) (pred self.thresh[1])] 1 risk[(pred self.thresh[1]) (pred self.thresh[2])] 2 risk[pred self.thresh[2]] 3 return risk def risk_area_percentage(self, risk_map): total risk_map.size high np.sum(risk_map 3) return high / total * 100
