Currently, desertification is a major problem in the western desert of Iraq. The harsh nature, remoteness, and size of the desert make it difficult and expensive to monitor and mitigate desertification. Therefore, this study proposed a comprehensive and cost‐effective method, via the integration of geographic information systems (GISs) and remote sensing (RS) techniques to estimate the potential risk of desertification, to identify the most vulnerable areas and determine the most appropriate sites for rainwater conservation. Two indices, namely, the Normalized Differential Vegetation Index (NDVI) and Land Degradation Index (LDI), were used for a cadastral assessment of land degradation. The findings of the combined rainwater harvesting appropriateness map, and the maps of NDVI and LDI changes found that 65% of highly suitable land for rainwater harvesting lies in the large change and 35% lies in the small change of NDVI, and 85% of highly suitable land lies in areas with a moderate change and 12% lies in strong change of LDI. The adoption of the weighted linear combination (WLC) and Boolean methods within the GIS environment, and the analysis of NDVI with LDI changes can allow hydrologists, decision‐makers, and planners to quickly determine and minimize the risk of desertification and to prioritize the determination of suitable sites for rainwater harvesting.

The impact of the suspended sediment load (SSL) on environmental health, agricultural operations, and water resources planning, is significant. The deposit of SSL restricts the streamflow region, affecting aquatic life migration and finally causing a river course shift. As a result, data on suspended sediments and their fluctuations are essential for a number of authorities especially for water resources decision makers. SSL prediction is often difficult due to a number of issues such as site-specific data, site-specific models, lack of several substantial components to use in prediction, and complexity its pattern. In the past two decades, many machine learning algorithms have shown huge potential for SSL river prediction. However, these models did not provide very reliable results, which led to the conclusion that the accuracy of SSL prediction should be improved. As a result, in order to solve past concerns, this research proposes a Long Short-Term Memory (LSTM) model for SSL prediction. The proposed model was applied for SSL prediction in Johor River located in Malaysia. The study allocated data for suspended sediment load and river flow for period 2010 to 2020. In the current research, four alternative models—Multi-Layer Perceptron (MLP) neural network, Support Vector Regression (SVR), Random Forest (RF), and Long Short-term Memory (LSTM) were investigated to predict the suspended sediment load. The proposed model attained a high correlation value between predicted and actual SSL (0.97), with a minimum RMSE (148.4 ton/day and a minimum MAE (33.43 ton/day).