Search Results (3)

Population growth and human activities have resulted in drastic changes in land use in many areas of the world, including the Hyrcania region in northern Iran. Land-use changes affect the hydrological processes of water basins. This study evaluated the effect of land-use changes on runoff over 15 years in the Haraz River basin located in Hyrcania using remote sensing data and GIS analyses. The annual precipitation of the region is 66.5 cm. Two Landsat images were used to develop land-use maps for 1996 and 2011. Original image features, their principal components, and vegetation indices were used to classify the two Landsat images into different land-use categories. Runoff was predicted from precipitation, land use, and hydrological soil groups, using the SCS-CN model (the “curve number” approach). During the 15 years, 62.4% of the area remained unchanged and 37.6% had undergone a land-use change. The highest average runoffs were obtained for bare land (14.1–14.5 cm/year) and residential land (10.4–11.4 cm/year), and the lowest for dense forest (2.5–2.6 cm/year) and first-grade rangeland (2.8–3.1 cm/year). The volume of annual runoff increased by 9% during 1996–2011 due to land-use changes. Runoff was estimated at 9.4% of precipitation in 1996, and 9.6% of precipitation in 2011. Most of the increase was related to the increased area of bare land and decreased area of rangeland. The study indicated that combined use of the SCS-CN approach, remote sensing data, and GIS tools allow cost-effective runoff estimation, helping watershed management. The results on the effect of land-use change on runoff can be seen as a warning for land-use managers and policymakers, who should aim at stopping and reversing the current land-use trends of the Haraz River basin. Full article

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas. Full article

This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains. Full article