In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO-ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO-ANN = 0.773) the landslide pattern.
Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.
Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.
Lymnaeid snails play a crucial role in the transmission of trematode cercariae as an intermediate host that can infect humans, ruminants like buffalo, and other animals, resulting in serious economic losses. The purpose of the study was to identify the morphological and molecular characteristics of snails and cercariae collected from water bodies near buffalo farms that were integrated with palm oil in Perak, Malaysia. The presence or absence of snails in 35 water bodies was examined via cross-sectional study. From three marsh wetlands, 836 lymnaeid snails were gathered in total. Each snail's shell was morphologically identified to determine its family and species. The cercarial stage inside each snail's body was observed using the crushing method and trematode cercariae types were determined. In addition, the target gene Cytochrome c oxidase subunit 1 (Cox1) and the ribosomal internal transcribed spacer 2 (ITS2) region were used to identify the snail species and cercarial types according to the species level. The findings indicated that the collected snails belong to the family lymnaeidae and Radix rubiginosa species. In snails, the cercarial emergence infection rate was 8.7%. Echinostome, xiphidiocercariae, gymnocephalous, brevifurcate-apharyngeate distome cercariae (BADC), and longifurcate-pharyngeal monostome cercariae (LPMC) are the five morphological cercarial types that were observed. The cercariae were identified using morphological and molecular techniques, and they are members of the four families which are Echinostomatidae, Plagiorchiidae, Fasciolidae, and Schistosomatidae. Interestingly, this is the first study on R. rubiginosa and several trematode cercariae in Perak water bodies near buffalo farms that are integrated with palm oil. In conclusion, our research shown that a variety of parasitic trematodes in Perak use R. rubiginosa as an intermediate host.
Rumen flukes cause heavy economic losses in the ruminant industry worldwide, especially in tropical and subtropical countries. This study estimated the prevalence of rumen flukes in buffaloes, identified the species diversity, and determined risk factors associated with rumen fluke prevalence in Perak, Peninsular Malaysia. A cross-sectional study was conducted, and 321 faecal samples were collected from six buffalo farms. A structured questionnaire was developed, and farmers were interviewed to obtain information regarding risk factors associated with rumen fluke infection. The faecal samples were examined using sedimentation and Flukefinder® techniques. Genomic DNA was extracted from the fluke eggs recovered using the Flukefinder® method, and the internal transcribed spacer 2 (ITS2) fragment was amplified and sequenced to facilitate species identification. The results showed that the overall prevalence of rumen fluke across the sampled farms was 40.2% (129/321). Three rumen fluke species were identified, namely, Fischoederius elongatus, F. cobboldi, and Orthocoelium streptocoelium. Several management factors had a significant association (P