Capability assessment and tourism development model verification of Haraz watershed using analytical hierarchy process (AHP)

The present study aimed at assessing tourism potential of a place to meet requirements of sustainable development policies. We studied the Haraz watershed because of its particular environmental characteristics and a high potential for ecotourism. The required data for this descriptive-analytical research were collected by combining field and desktop studies. First, the ecotourism capability assessment of the area was done using Arc GIS 10.3 software based on the Hyrcanian Forest Tourism Development Model for concentrated tourism and extensive tourism. Next, the most important effective indices included (i.e., 19 indices) were determined by Delphi questionnaire and SPSS 17. Finally, AHP technique was applied to analyze the body mass of the indices in order to verify the validity of the model. The results show that 0.0044, 01.3, 3.52, and 37.71% of the study area is suitable for concentrated ecotourism (grade 1), concentrated ecotourism (grade 2), extensive ecotourism (grade 1), and extensive ecotourism (grade 2), respectively. Based on the model applied, slope, direction, and fundamentals (infrastructure) with the body masses of 0.232, 0.116, and 0.115 were identified as the first priorities. Comparing the results of this model and AHP confirms the validity of the model. To strengthen the tourism development potential of the watershed and protect its ecosystems and biodiversity, it is necessary to choose a proper development model. Failure to identify the existing capacities and the field's sensitivities can cause dissatisfaction of local residents and also damage to the ecosystem of the area.

Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping

In this research, eight individual machine learning and statistical models are implemented and compared, and based on their results, seven ensemble models for flood susceptibility assessment are introduced. The individual models included artificial neural networks, classification and regression trees, flexible discriminant analysis, generalized linear model, generalized additive model, boosted regression trees, multivariate adaptive regression splines, and maximum entropy, and the ensemble models were Ensemble Model committee averaging (EMca), Ensemble Model confidence interval Inferior (EMciInf), Ensemble Model confidence interval Superior (EMciSup), Ensemble Model to estimate the coefficient of variation (EMcv), Ensemble Model to estimate the mean (EMmean), Ensemble Model to estimate the median (EMmedian), and Ensemble Model based on weighted mean (EMwmean). The data set covered 201 flood events in the Haraz watershed (Mazandaran province in Iran) and 10,000 randomly selected non-occurrence points. Among the individual models, the Area Under the Receiver Operating Characteristic (AUROC), which showed the highest value, belonged to boosted regression trees (0.975) and the lowest value was recorded for generalized linear model (0.642). On the other hand, the proposed EMmedian resulted in the highest accuracy (0.976) among all models. In spite of the outstanding performance of some models, nevertheless, variability among the prediction of individual models was considerable. Therefore, to reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment.

River suspended sediment modelling using the CART model: A comparative study of machine learning techniques

Suspended sediment load (SSL) modelling is an important issue in integrated environmental and water resources management, as sediment affects water quality and aquatic habitats. Although classification and regression tree (CART) algorithms have been applied successfully to ecological and geomorphological modelling, their applicability to SSL estimation in rivers has not yet been investigated. In this study, we evaluated use of a CART model to estimate SSL based on hydro-meteorological data. We also compared the accuracy of the CART model with that of the four most commonly used models for time series modelling of SSL, i.e. adaptive neuro-fuzzy inference system (ANFIS), multi-layer perceptron (MLP) neural network and two kernels of support vector machines (RBF-SVM and P-SVM). The models were calibrated using river discharge, stage, rainfall and monthly SSL data for the Kareh-Sang River gauging station in the Haraz watershed in northern Iran, where sediment transport is a considerable issue. In addition, different combinations of input data with various time lags were explored to estimate SSL. The best input combination was identified through trial and error, percent bias (PBIAS), Taylor diagrams and violin plots for each model. For evaluating the capability of the models, different statistics such as Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and percent bias (PBIAS) were used. The results showed that the CART model performed best in predicting SSL (NSE=0.77, KGE=0.8, PBIAS<±15), followed by RBF-SVM (NSE=0.68, KGE=0.72, PBIAS<±15). Thus the CART model can be a helpful tool in basins where hydro-meteorological data are readily available.